Welcome to OpenREM’s documentation¶

OpenREM is a free, open source application for patient radiation dose monitoring. The software is capable of importing and displaying data from a wide variety of x-ray dose related sources with filtering, charts and analysis. The software also enables easy export of the data in a form that is suitable for further analysis by suitably qualified medical physics personnel.

Please see openrem.org for more details.

These docs are for the version of OpenREM currently in development. For the current release version, see https://docs.openrem.org/

These docs are in British English - if you want to help translate them into other languages please get in touch: @_OpenREM, Bitbucket, Google Groups or head over to Weblate to get started. More details: Translating OpenREM strings.

Contents:

Installation¶

Installation options¶

There are three supported installation options for OpenREM v1.0:

Docker
Native install on Linux
Native install on Windows

Docker¶

This is the quickest and easiest way of installing a fully functioning OpenREM instance, complete with database, web server and DICOM server, on any operating system that supports Docker with Linux containers. This includes Windows 10 with Docker Desktop, but currently excludes Windows Server, though this may change with availability of WSL2 for Windows Server 2022.

The Docker installation has mostly been tested with Ubuntu server, but has also been used successfully with Podman on Redhat Enterprise Linux and other distributions.

Existing Windows or Linux installations of OpenREM 0.10 can be upgraded to run in a Docker installation.

It is advisable that the server OpenREM is installed on has access to the internet to get images from Docker and security updates for the operating system. However, if this is not possible the Docker images can be obtained on a computer that does have access to the internet and transferred to the ‘offline’ server for installation.

Docker install¶

Preparation¶

Install Docker and Docker Compose (may be installed automatically with Docker)
Download https://bitbucket.org/openrem/docker/get/1.0.0b2.zip

Install¶

Extract the ZIP file and open a shell (command prompt) in the new folder
Customise variables in the following two files:
- .env.prod
- the orthanc_1 section of docker-compose.yml
If you are using SELinux, you will also need to edit the nginx and orthanc bind mounts in docker-compose.yml

A full description of the options are found in:

Docker env configuration¶

Edit the .env.prod file to customise your installation. There should be no space between the variable name, the = and the value. Everything after the = until the end of the line is transferred as the value. These settings take effect when docker-compose is started or restarted.

Variables that should always be changed¶

Set a new secret key. Create your own, or generate one by using a tool like http://www.miniwebtool.com/django-secret-key-generator/ for this:

SECRET_KEY=

DJANGO_ALLOWED_HOSTS is a string of hostnames or IPs with a space between each:

nginx is required for internal use
localhost 127.0.0.1 [::] allows access on the server using the localhost name or IP (using IPv4 or IPv6)
add the name and/or IP address of your server so it can be accessed from other computers on your network.

For example: DJANGO_ALLOWED_HOSTS=nginx localhost 127.0.0.1 [::1] myservername

DJANGO_ALLOWED_HOSTS=nginx localhost 127.0.0.1 [::1]

Variables to help with debugging problems¶

Set to 1 to enable Django debugging mode.

DEBUG=

Set the log level. Options are DEBUG, INFO, WARNING, ERROR, and CRITICAL, with progressively less logging.

LOG_LEVEL=
LOG_LEVEL_QRSCU=
LOG_LEVEL_EXTRACTOR=

Variables to be changed for your environment¶

E-mail server settings¶

EMAIL_HOST=
EMAIL_PORT=
EMAIL_HOST_USER=
EMAIL_HOST_PASSWORD=
EMAIL_USE_TLS=
EMAIL_USE_SSL=
EMAIL_DOSE_ALERT_SENDER=
EMAIL_OPENREM_URL=

The host name and port of the e-mail server that you wish to use must be entered in the EMAIL_HOST and EMAIL_PORT fields. EMAIL_HOST might be your institution’s Outlook/Exchange server.

If the e-mail server is set to only allow authenticated users to send messages then a suitable user and password must be entered in the EMAIL_HOST_USER and EMAIL_HOST_PASSWORD fields. If this approach is used then it may be useful to request that an e-mail account be created specifically for sending these OpenREM alert messages.

It may be possible to configure the e-mail server to allow sending of messages that originate from the OpenREM server without authentication, in which case the user and password settings should not be required.

The EMAIL_USE_TLS and EMAIL_USE_SSL options should be configured to match the encryption requirements of the e-mail server. Use 0 for False (default) and 1 for True. Only one of these options should be set to 1.

The EMAIL_DOSE_ALERT_SENDER should contain the e-mail address that you want to use as the sender address.

The EMAIL_OPENREM_URL must contain the URL of your OpenREM installation in order for hyperlinks in the e-mail alert messages to function correctly.

Regionalisation¶

Local time zone for this installation. Choices can be found here: http://en.wikipedia.org/wiki/List_of_tz_zones_by_name although not all choices may be available on all operating systems:

TIME_ZONE=Europe/London

Language code for this installation. All choices can be found here: http://www.i18nguy.com/unicode/language-identifiers.html

LANGUAGE_CODE=en-us

If you set this to False, Django will make some optimizations so as not to load the internationalization machinery:

USE_I18N=True

If you set this to False, Django will not format dates, numbers and calendars according to the current locale:

USE_L10N=True

If you set this to False (default), Django will not use timezone-aware datetimes:

USE_TZ=False

XLSX date and time settings for exports:

XLSX_DATE=dd/mm/yyyy
XLSX_TIME=hh:mm:ss

Virtual directory settings¶

See Running the OpenREM website in a virtual directory for details of these variables - normally these can be left commented out.

Device Observer UID settings¶

OpenREM users have found one x-ray system which incorrectly sets the Device Observer UID to be equal to the Study Instance UID. In this situation a new entry is created in the display name settings for every new exam that arrives in OpenREM, making the display name table fill with many duplicate entries for the same system. To avoid this problem a list of models can be specified using the variable below - OpenREM will ignore the Device Observer UID value when creating new display names for any model in this list. The model name text must exactly match what is contained in the system’s Manufacturer’s Model Name DICOM tag (0008,1090).

IGNORE_DEVICE_OBSERVER_UID_FOR_THESE_MODELS = ['GE OEC Fluorostar']

Variables that should only be changed if you know what you are doing¶

## Database settings
SQL_HOST=db
SQL_ENGINE=django.db.backends.postgresql
SQL_PORT=5432
DATABASE=postgres
POSTGRES_USER=openremuser
POSTGRES_PASSWORD=openrem_pass
POSTGRES_DB=openrem_prod

## Paths
MEDIA_ROOT=/home/app/openrem/mediafiles
STATIC_ROOT=/home/app/openrem/staticfiles
LOG_ROOT=/logs

Variables that shouldn’t be changed¶

Changing this will mean some OpenREM functions will fail

DOCKER_INSTALL=1

DICOM store configuration (Orthanc)¶

Orthanc provides the DICOM Store functionality to enable scanners to send directly to OpenREM, and for query-retrieve to function. Configuration is in the orthanc section of docker-compose.yml

OpenREM Lua script configuration¶

This file is formatted as YAML:

Strings need to quoted or placed on a new line after a |
A : and a space separate the variable name and the value, and spaces are used at the start of the line to create a hierarchy. See the examples below.

Edit the docker-compose.yml file to make the changes. They will take effect next time docker-compose up -d is run.

Find the orthanc_1 definition near the end of the file.

Objects to be ignored¶

Lists of things to ignore. Orthanc will ignore anything matching the content of these comma separated lists: they will not be imported into OpenREM. Some examples have been added below - note the formatting syntax. STATION_NAMES_TO_IGNORE has the value on a new line with a | instead of being quoted, to show this syntax option:

environment:
  MANUFACTURERS_TO_IGNORE: "{'Faxitron X-Ray LLC', 'Gendex-KaVo'}"
  MODEL_NAMES_TO_IGNORE: "{'CR 85', 'CR 75'}"
  STATION_NAMES_TO_IGNORE: |
    {'CR85 Main', 'CR75 Main'}
  SOFTWARE_VERSIONS_TO_IGNORE: "{'VixWin Platinum v3.3'}"
  DEVICE_SERIAL_NUMBERS_TO_IGNORE: "{'SCB1312016'}"

Extractor for older Toshiba CT dose summary files¶

Enable or disable additional functionality to extract dose information from older Toshiba and GE scanners, and specify which CT scanners should use this method. Each system should be listed as {'Manufacturer', 'Model name'}, with systems in a comma separated list within curly brackets, as per the example below:

environment:
  USE_TOSHIBA_CT_EXTRACTOR: "true"
  TOSHIBA_EXTRACTOR_SYSTEMS: |
    {{'Toshiba', 'Aquilion'}, {'GE Medical Systems', 'Discovery STE'},}

Physics Filtering¶

Set this to true if you want Orthanc to keep physics test studies, and have it put them in the imports/physics/ folder. Set it to "false" to disable this feature

environment:
  USE_PHYSICS_FILTERING: "true"

A list to check against patient name and ID to see if the images should be kept.

environment:
  PHYSICS_TO_KEEP: "{'physics',}"

Orthanc Configuration¶

This section is formatted as JSON. It can contain any configuration options that appear in the standard Orthanc orthanc.json file, but the ones that are needed for OpenREM are included as standard and described below.

Strings need to quoted with double quotes ".

DICOM Application Entity Title¶

Application Entity Title of the Store Server. Should be up to 16 characters, no spaces. This server isn’t fussy by default, so if remote nodes connect using a different AETitle that is ok.

ORTHANC_JSON: |
  {
    // DICOM Store configuration
    "DicomAet" : "OPENREM",
  }

DICOM Port¶

The default port for DICOM store is set to 104.

To use a different port, change the first number of the pair in ports. The first number is the port exposed outside of Docker, the second number is used internally by the Orthanc container.

For example, to use port 8104:

ports:
# DICOM store port (first number)
  - 8104:4242

Orthanc web interface¶

There will normally not be any studies in the Orthanc database once they have been processed, but if you want to enable the Orthanc web viewer, enable the port in and set RemoteAccessAllowed to true in the ORTHANC_JSON section. The first number in the port configuration can be changed if required:

ports:
# Othanc web interface
  - 8042:8042

ORTHANC_JSON: |
  {
    "Name" : "OpenREM Orthanc",
    "RemoteAccessAllowed" : true,
    "AuthenticationEnabled" : true,
    "RegisteredUsers" : {
      "orthancuser": "demo"
    },
  }

Lua script path¶

The path within the Orthanc container for the OpenREM Lua script is specified here - this should not be changed (see below for advanced options).

ORTHANC_JSON: |
  {
    // OpenREM Lua Script
    "LuaScripts" : [
      "/etc/share/orthanc/scripts/openrem_orthanc_config_docker.lua"
    ]
  }

Advanced options¶

Multiple stores¶

If you need more than one DICOM Store server, to listen on a different port for example, copy the whole orthanc_1 section in docker-compose.yml and paste it after the orthanc_1 block. Rename to orthanc_2.

Next time docker-compose is started the additional Orthanc container will be started. docker-compose.yml is also used to stop the containers, so if you are removing the additional Orthanc container stop the containers first.

Advanced Orthanc configuration¶

Any of the Orthanc configuration settings can be set in the ORTHANC_JSON section. The default configuration can be seen on the Orthanc Server webpages including documentation as to how they are used.

A custom version of the openrem_orthanc_config_docker.lua script can be used if required. Copy the existing one and place the new one, with a new name, in the orthanc/ folder, and set the LuaScripts value in ORTHANC_JSON to match.

Pay special attention to the first sections, up to the ToAscii function, these sections have been changed for the Docker implementation.

Docker SELinux configuration¶

SELinux will prevent bind mounts in Docker with the standard configuration, which will be seen because Orthanc fails to start. SELinux is commonly enabled on Red Hat, Fedora and associated distributions.

The docker-compose.yml file needs to be edited to fix this.

Change nginx configuration¶

Find the following section:

nginx:
  container_name: openrem-nginx
  restart: unless-stopped
  image: nginx:1.17.8-alpine
  volumes:
    - media_volume:/home/app/openrem/mediafiles
    - static_volume:/home/app/openrem/staticfiles
# For SELinux (RedHat, Fedora etc), add :z to the end of next two lines
    - ./nginx-conf/conf.d:/etc/nginx/conf.d
    - ./nginx-conf/certs:/etc/ssl/private

Follow the instruction to edit the nginx-conf lines, like this:

# For SELinux (RedHat, Fedora etc), add :z to the end of next two lines
    - ./nginx-conf/conf.d:/etc/nginx/conf.d:z
    - ./nginx-conf/certs:/etc/ssl/private:z

Change the Orthanc configuration¶

Find the following section:

orthanc_1:
  container_name: openrem-orthanc-1
  restart: unless-stopped
  image: openrem/orthanc
  volumes:
    - imports_volume:/imports
# For SELinux (RedHat, Fedora etc), add :z to the end of next line
    - ./orthanc:/etc/share/orthanc/scripts/

Follow the instruction to edit the orthanc_1 line, like this:

# For SELinux (RedHat, Fedora etc), add :z to the end of next line
    - ./orthanc:/etc/share/orthanc/scripts/:z

Start the containers with:

$ docker-compose up -d

Get the database and translations ready:

$ docker-compose exec openrem python manage.py makemigrations remapp --noinput
$ docker-compose exec openrem python manage.py migrate --noinput
$ docker-compose exec openrem python manage.py loaddata openskin_safelist.json
$ docker-compose exec openrem python manage.py collectstatic --noinput --clear
$ docker-compose exec openrem python manage.py compilemessages
$ docker-compose exec openrem python manage.py createsuperuser

Open a web browser and go to http://localhost/

If you want to run the OpenREM in a virtual directory (like http://server/dms/) there is further configuration to be done - go to Running the OpenREM website in a virtual directory.

Upgrade to Docker¶

These instructions assume:

You are upgrading from 0.10.0.
You are using a PostgreSQL database in the existing installation.
That existing Linux installs followed the instructions in the previous releases, with the openrem-function format that changed in the 0.9.1 release (Systemd service names in Ubuntu installs).

If not you will need to adapt the instructions as necessary.

Upgrades from 0.9.1 or earlier should review Upgrade to OpenREM 0.10.0 from 0.7.3 or later. – needs changing

Upgrade to OpenREM 0.10.0 from 0.7.3 or later¶

Upgrades to OpenREM 1.0 can only be made from version 0.10.0. Installations earlier than that need to be updated to version 0.10.0 before updating to version 1.0.

These instructions can be used to upgrade any database from version 0.7.3 or later. 0.7.3 was released in August 2016. For upgrades from versions earlier than that, please review the upgrade instructions for that version in the 0.10.0-docs.

Upgrade preparation¶

Python 2.7.9 or later must be installed, but it must still be Python 2.7 and not any of the Python 3 releases.

To check the Python version, activate the virtualenv if you are using one, then:

$ python -V

If the version is earlier than 2.7.9, then an upgrade is needed. If the version is 3.x, then Python 2.7 must be installed.

Ubuntu Linux

Check which version of Ubuntu is installed (lsb_release -a)
If it is 14.04 LTS (Trusty), then an operating system upgrade or migration to a new server is required. If migrating, ensure the version of OpenREM installed on the new server is the same as the one on the old server, then Database restore following the instructions and when up and running again perform the upgrade on the new server
16.04 LTS (Xenial) or later should have 2.7.11 or later available.
For other Linux distributions check in their archives for which versions are available.

Windows

A newer version of Python 2.7 can be downloaded from python.org and installed over the current version.

Linux and Windows

With a version of Python 2.7.9 or later, setuptools can be updated (activate virtualenv if using one):
$ pip install setuptools -U

Upgrade¶

Back up your database
- For PostgreSQL on linux you can refer to Database backup
- For PostgreSQL on Windows you can refer to Windows installations
- For a non-production SQLite3 database, simply make a copy of the database file
Stop any Celery workers
Consider temporarily disabling your DICOM Store SCP, or redirecting the data to be processed later
If you are using a virtualenv, activate it
Install specific versions of some packages that are needed:

$ pip install django-crispy-forms==1.8.1
$ pip install django-solo==1.1.5
$ pip install flower==0.9.5

Install specific version of Celery:

Linux server:

$ pip install celery==4.2.2

Windows server:

D:\>pip install celery==3.1.25

Install the new version of OpenREM:
$ pip install openrem==0.10.0

Update the configuration¶

Locate and edit your local_settings file

Ubuntu linux: /usr/local/lib/python2.7/dist-packages/openrem/openremproject/local_settings.py
Other linux: /usr/lib/python2.7/site-packages/openrem/openremproject/local_settings.py
Linux virtualenv: vitualenvfolder/lib/python2.7/site-packages/openrem/openremproject/local_settings.py
Windows: C:\Python27\Lib\site-packages\openrem\openremproject\local_settings.py
Windows virtualenv: virtualenvfolder\Lib\site-packages\openrem\openremproject\local_settings.py

Add additional log file configuration - changed with 0.8¶

Add the new extractor log file configuration to the local_settings.py - you can copy the ‘Logging configuration’ section here if you haven’t made any changes. The addition that needs to be inserted are the lines relating to the extractor log file. This is only for upgrading the database - the local_settings.py file will be updated again for the upgrade to 1.0:

# Logging configuration
# Set the log file location. The example places the log file in the media directory. Change as required - on linux
# systems you might put these in a subdirectory of /var/log/. If you want all the logs in one file, set the filename
# to be the same for each one.
import os
LOG_ROOT = MEDIA_ROOT
logfilename = os.path.join(LOG_ROOT, "openrem.log")
qrfilename = os.path.join(LOG_ROOT, "openrem_qr.log")
storefilename = os.path.join(LOG_ROOT, "openrem_store.log")
extractorfilename = os.path.join(LOG_ROOT, "openrem_extractor.log")

LOGGING['handlers']['file']['filename'] = logfilename          # General logs
LOGGING['handlers']['qr_file']['filename'] = qrfilename        # Query Retrieve SCU logs
LOGGING['handlers']['store_file']['filename'] = storefilename  # Store SCP logs
LOGGING['handlers']['extractor_file']['filename'] = extractorfilename  # Extractor logs

# Set log message format. Options are 'verbose' or 'simple'. Recommend leaving as 'verbose'.
LOGGING['handlers']['file']['formatter'] = 'verbose'        # General logs
LOGGING['handlers']['qr_file']['formatter'] = 'verbose'     # Query Retrieve SCU logs
LOGGING['handlers']['store_file']['formatter'] = 'verbose'  # Store SCP logs
LOGGING['handlers']['extractor_file']['formatter'] = 'verbose'  # Extractor logs

# Set the log level. Options are 'DEBUG', 'INFO', 'WARNING', 'ERROR', and 'CRITICAL', with progressively less logging.
LOGGING['loggers']['remapp']['level'] = 'INFO'                    # General logs
LOGGING['loggers']['remapp.netdicom.qrscu']['level'] = 'INFO'     # Query Retrieve SCU logs
LOGGING['loggers']['remapp.netdicom.storescp']['level'] = 'INFO'  # Store SCP logs
LOGGING['loggers']['remapp.extractors.ct_toshiba']['level'] = 'INFO'  # Toshiba RDSR creation extractor logs

Migrate the database¶

In a shell/command window, move into the openrem folder:

Ubuntu linux: /usr/local/lib/python2.7/dist-packages/openrem/
Other linux: /usr/lib/python2.7/site-packages/openrem/
Linux virtualenv: vitualenvfolder/lib/python2.7/site-packages/openrem/
Windows: C:\Python27\Lib\site-packages\openrem\
Windows virtualenv: virtualenvfolder\Lib\site-packages\openrem\

python manage.py makemigrations remapp
python manage.py migrate remapp

Systemd service names in Ubuntu installs¶

Systemd service files were renamed in the the 0.9.1 docs to use openrem-function rather than function-openrem. To update the service files accordingly, follow the following steps. This is optional, but will make finding them easier (e.g. sudo systemctl status openrem-[tab][tab] will list them) and these names are assumed for the Upgrade to Docker and Upgrading a native Linux install docs. However, only the gunicorn service remains after the upgrade to 1.0, so you may find it easier just to remember the only service names, or just rename that one.

sudo systemctl stop gunicorn-openrem.service
sudo systemctl stop celery-openrem.service
sudo systemctl stop flower-openrem.service

sudo systemctl disable gunicorn-openrem.service
sudo systemctl disable celery-openrem.service
sudo systemctl disable flower-openrem.service

sudo mv /etc/systemd/system/{gunicorn-openrem,openrem-gunicorn}.service
sudo mv /etc/systemd/system/{celery-openrem,openrem-celery}.service
sudo mv /etc/systemd/system/{flower-openrem,openrem-flower}.service

sudo systemctl enable openrem-gunicorn.service
sudo systemctl enable openrem-celery.service
sudo systemctl enable openrem-flower.service

sudo systemctl start openrem-gunicorn.service
sudo systemctl start openrem-celery.service
sudo systemctl start openrem-flower.service

Upgrade to 1.0¶

Now return to Installation instructions to follow the instructions to 1.0 for your preferred server solution.

After upgrading to version 1.0, there will be automatic tasks that are created to populate the summary fields introduced in version 0.10.

Log in as an administrator to start the migration process. If you have a large number of studies in your database this can take some time. A large database (several hundred studies) on slow disks might take a day or two, on faster disks or with a smaller database it could take from a few minutes to an hour or so. You will be able to monitor the progress on the home page as seen in the figure at the bottom of this page.

0.10 upgrade panel after log in as administrator

One task per modality type (CT, fluoroscopy, mammography and radiography) is generated to create a task per study in each modality to populate the new fields for that study. If the number of workers is the same or less than the number of modality types in your database then the study level tasks will all be created before any of them are executed as all the workers will be busy. Therefore there might be a delay before the progress indicators on the OpenREM front page start to update. You can review the number of tasks being created on the Config -> Tasks page.

Before the migration is complete, some of the information on the modality pages of OpenREM will be missing, such as the dose information for example, but otherwise everything that doesn’t rely on Celery workers will work as normal. Studies sent directly to be imported will carry on during the migration, but query-retrieve tasks will get stuck behind the migration tasks.

0.10 upgrade panel, population of fields in progress

When the process is complete the ‘Summary data fields migration’ panel will disappear and will not be seen again.

Upgrade process from a PostgresQL database¶

Stop the existing services¶

Linux:

$ sudo systemctl stop orthanc
$ sudo systemctl stop nginx
$ sudo systemctl stop openrem-gunicorn
$ sudo systemctl stop openrem-flower
$ sudo systemctl stop openrem-celery
$ sudo systemctl stop rabbitmq-server
$ sudo systemctl disable orthanc
$ sudo systemctl disable nginx
$ sudo systemctl disable openrem-gunicorn
$ sudo systemctl disable openrem-flower
$ sudo systemctl disable openrem-celery
$ sudo systemctl disable rabbitmq-server

Windows: stop the following services
- Orthanc or Conquest
- IIS OpenREM site or other webserver
- Flower
- Celery
- RabbitMQ

Establish existing database details¶

Review the current local_settings.py for the database settings and location of the MEDIA_ROOT folder. The file is in:

Ubuntu linux: /usr/local/lib/python2.7/dist-packages/openrem/openremproject/local_settings.py
Other linux: /usr/lib/python2.7/site-packages/openrem/openremproject/local_settings.py
Linux virtualenv: vitualenvfolder/lib/python2.7/site-packages/openrem/openremproject/local_settings.py
Windows: C:\Python27\Lib\site-packages\openrem\openremproject\local_settings.py
Windows virtualenv: virtualenvfolder\Lib\site-packages\openrem\openremproject\local_settings.py

Export the database¶

Open a command line window
Windows: go to Postgres bin folder, for example:
$ cd "C:\Program Files\PostgreSQL\9.6\bin"
Dump the database:
- Use the username (-U openremuser) and database name (-d openremdb) from local_settings.py
- Use the password from local_settings.py when prompted
- For linux, the command is pg_dump (no .exe)
- Set the path to somewhere suitable to dump the exported database file
$ pg_dump.exe -U openremuser -d openremdb -F c -f path/to/export/openremdump.bak

Set up the new installation¶

Install Docker
Download and extract https://bitbucket.org/openrem/docker/get/develop.zip and open a shell (command window) in the new folder
Customise variables in .env.prod, the orthanc_1 section in docker-compose.yml and in orthanc_1.json as necessary. A full description of the options are found in:

Start the containers with:

$ docker-compose up -d

Copy the database backup to the postgres docker container and import it. If you have changed the database variables, ensure that:

the database user (-U openremuser) matches POSTGRES_USER in .env.prod
the database name (-d openrem_prod) matches POSTGRES_DB in .env.prod

They don’t have to match the old database settings. The filename in both commands (openremdump.bak) should match your backup filename.

$ docker cp /path/to/openremdump.bak openrem-db:/db_backup/

$ docker-compose exec db pg_restore --no-privileges --no-owner -U openremuser -d openrem_prod /db_backup/openremdump.bak

It is normal to get an error about the public schema, for example:

pg_restore: while PROCESSING TOC:
pg_restore: from TOC entry 3; 2615 2200 SCHEMA public postgres
pg_restore: error: could not execute query: ERROR:  schema "public" already exists
Command was: CREATE SCHEMA public;

pg_restore: warning: errors ignored on restore: 1

Rename the 0.10 upgrade migration file, migrate the database (the steps and fakes are required as it is not a new database), and create the static files:

$ docker-compose exec openrem mv remapp/migrations/0001_initial.py.1-0-upgrade remapp/migrations/0001_initial.py

$ docker-compose exec openrem python manage.py migrate --fake-initial

$ docker-compose exec openrem python manage.py migrate remapp --fake

$ docker-compose exec openrem python manage.py makemigrations remapp

$ docker-compose exec openrem python manage.py migrate

$ docker-compose exec openrem python manage.py loaddata openskin_safelist.json

$ docker-compose exec openrem python manage.py collectstatic --noinput --clear

Generate translation binary files

$ docker-compose exec openrem python manage.py compilemessages

The new OpenREM installation should now be ready to be used.

Offline Docker installation¶

OpenREM can be run on a server that is not connected to the internet if required, though access to https://hub.docker.com would make installation and upgrades much easier.

The server will need to have Docker and Docker Compose installed.

Collect installation files¶

On a computer with internet access:

Install Docker - this is required to download the images
Download https://bitbucket.org/openrem/docker/get/develop.zip
Download the Docker images:

$ docker pull openrem/openrem:release-1.0.0b2

$ docker pull postgres:12.0-alpine

$ docker pull openrem/nginx

$ docker pull rabbitmq:3-management-alpine

$ docker pull openrem/orthanc

Now save them as tar files:

$ docker save -o openrem.tar openrem/openrem:develop

$ docker save -o openrem-postgres.tar postgres:12.0-alpine

$ docker save -o openrem-nginx.tar openrem/nginx

$ docker save -o openrem-rabbitmq.tar rabbitmq:3-management-alpine

$ docker save -o openrem-orthanc.tar openrem/orthanc

If both the computer with internet access and the target server are Linux or MacOS the images can be made smaller using gzip, for example:

$ docker save openrem/openrem:develop | gzip > openrem.tar.gz

Copy all the tar files and the zip file to the server where OpenREM is to be installed.

Load the docker images¶

On the server where OpenREM is to be installed, in the folder containing the Docker images:

$ docker load -i openrem.tar

$ docker load -i openrem-postgres.tar

$ docker load -i openrem-nginx.tar

$ docker load -i openrem-rabbitmq.tar

$ docker load -i openrem-orthanc.tar

If you have compressed the images with gzip the command is the same but with the .gz suffix, for example:

$ docker load -i openrem.tar.gz

Check that the images have been loaded:

$ docker images

Continue to Install

Native install on Linux¶

A native installation on Linux requires Python, a webserver (eg Nginx) a database (ideally PostgreSQL) and a DICOM server (ideally Orthanc) to be installed, with OpenREM and all the other dependencies being installed via Pip.

Existing installations of OpenREM 0.10 can be upgraded, but this release requires a different version of Python to the older releases, and some services that were previously required are no longer needed. Full upgrade instructions are provided, based on an Ubuntu Server installation.

Native Linux install¶

Document not ready for translation

This install is based on Ubuntu 22.04 using:

Python 3.10 running in a virtualenv
Database: PostgreSQL
DICOM Store SCP: Orthanc running on port 104
Webserver: NGINX with Gunicorn
All OpenREM files in /var/dose/ with group owner of openrem
Collects any Physics (QA) images and zips them

The instructions should work for Ubuntu 20.04 too, references to jammy will be focal instead.

There are various commands and paths that reference the Python version 3.10 in these instructions. If you are using Python 3.8 or Python 3.9 then these will need to be modified accordingly.

If you are upgrading an existing installation to a new Linux server, go to the Upgrading to a new Linux server docs first.

If you are installing OpenREM on a Linux server with limited internet access, go to the Offline installation or upgrade docs.

If you are installing on a different Linux OS you can adapt these instructions or consider using a Docker install instead.

Initial prep¶

Install apt packages¶

Apt sources

We will need the universe repository enabled. Check first:

$ less /etc/apt/sources.list

Look for:

deb http://archive.ubuntu.com/ubuntu/ jammy universe
deb http://archive.ubuntu.com/ubuntu/ jammy-updates universe

If these two lines are not there or are commented out (line starts with a #), add them in or remove the # (sudo nano /etc/apt/sources.list).

$ sudo -- sh -c 'apt update && apt upgrade'

$ sudo apt install acl python3.10 python3.10-dev python3.10-distutils python3.10-venv python3-pip \
postgresql nginx orthanc dcmtk default-jre zip gettext

Redis

Redis is used to temporarily store the background tasks.

$ sudo apt install lsb-release

$ curl -fsSL https://packages.redis.io/gpg | sudo gpg --dearmor -o /usr/share/keyrings/redis-archive-keyring.gpg
$ echo "deb [signed-by=/usr/share/keyrings/redis-archive-keyring.gpg] https://packages.redis.io/deb $(lsb_release -cs) main" | sudo tee /etc/apt/sources.list.d/redis.list
$ sudo apt-get update
$ sudo apt-get install redis

Folders and permissions¶

Groups

Now create new group openrem and add your user to it ($USER will automatically substitute for the user you are running as):

$ sudo groupadd openrem
$ sudo adduser $USER openrem

Add orthanc and www-data users to openrem group:

$ sudo -- sh -c 'adduser orthanc openrem && adduser www-data openrem'

Note

At a later stage, to add a second administrator just add them to the openrem group in the same way.

Folders

Create the folders we need, and set the permissions. The ‘sticky’ group setting and the access control list setting (setfacl) below will enable both orthanc user and www-data user as well as you and your colleagues to write to the logs and access the ‘Physics’ images etc:

$ sudo -- sh -c 'mkdir /var/dose && chmod 775 /var/dose'

$ sudo chown $USER:openrem /var/dose

$ cd /var/dose

$ mkdir {log,media,pixelmed,static,veopenrem3}

$ mkdir -p orthanc/dicom && mkdir -p orthanc/physics

$ sudo chown -R $USER:openrem /var/dose/*

$ sudo chmod -R g+s /var/dose/*

Find the uid of your user and the gid of the openrem group:

$ id
$ getent group openrem

Take note of the uid number and the gid in the third field of the group information and use it in the next command, replacing 1001 (user uid) and 1002 (openrem group gid) as appropriate:

$ sudo setfacl -PRdm u:1001:rwx,g:1002:rwx,o::r /var/dose/

Pixelmed download¶

$ cd /var/dose/pixelmed
$ wget http://www.dclunie.com/pixelmed/software/webstart/pixelmed.jar

Create the virtualenv¶

Create a virtualenv (Python local environment) in the folder we created:

$ python3.10 -m venv /var/dose/veopenrem3

Activate the virtualenv¶

Activate the virtualenv (note the . – you can also use the word source):

$ . /var/dose/veopenrem3/bin/activate

Install Python packages¶

$ pip install --upgrade pip

$ pip install openrem==1.0.0b2

Database and OpenREM config¶

Setup PostgreSQL database¶

Create a postgres user, and create the database. You will be asked to enter a new password (twice). This will be needed when configuring the local_settings.py file later:

$ sudo -u postgres createuser -P openremuser

$ sudo -u postgres createdb -T template1 -O openremuser -E 'UTF8' openremdb

For upgrades use a different template

If this is an upgrade to a new Linux server and not a new install, use template0 instead:

$ sudo -u postgres createdb -T template0 -O openremuser -E 'UTF8' openremdb

Update the PostgreSQL client authentication configuration. Add the following line anywhere near the bottom of the file, for example in the gap before # DO NOT DISABLE or anywhere in the table that follows. The number of spaces between each word is not important (one or more). If you are not using PostgreSQL 14 then substitute the version number in the file path.

$ sudo nano /etc/postgresql/14/main/pg_hba.conf

local   all     openremuser                 md5

Reload postgres:

$ sudo systemctl reload postgresql

Configure OpenREM¶

Navigate to the Python openrem folder and copy the example local_settings.py and wsgi.py files to remove the .linux and .example suffixes:

$ cd /var/dose/veopenrem3/lib/python3.10/site-packages/openrem/
$ cp openremproject/local_settings.py{.linux,}
$ cp openremproject/wsgi.py{.example,}

Edit local_settings.py as needed - make sure you change the PASSWORD, the SECRET_KEY (to anything, just change it), the ALLOWED_HOSTS list, regionalisation settings and the EMAIL configuration. You can modify the email settings later if necessary. Some settings are not shown here but are documented in the settings file or elsewhere in the docs. For details on the final variable see Systems where Device Observer UID is not static.

Upgrading to a new server

If you are upgrading to a new Linux server, review the local_settings.py file from the old server to copy over the NAME, USER and PASSWORD, ALLOWED_HOSTS list and the EMAIL configuration, and check all the other settings. Change the SECRET_KEY from the default, but it doesn’t have to match the one on the old server. For details on the final variable see Systems where Device Observer UID is not static.

$ nano openremproject/local_settings.py

DATABASES = {
    'default': {
        'ENGINE': 'django.db.backends.postgresql',
        'NAME': 'openremdb',
        'USER': 'openremuser',
        'PASSWORD': 'mysecretpassword',     # This is the password you set earlier
        'HOST': '',
        'PORT': '',
    }
}

MEDIA_ROOT = '/var/dose/media/'

STATIC_ROOT = '/var/dose/static/'
JS_REVERSE_OUTPUT_PATH = os.path.join(STATIC_ROOT, 'js', 'django_reverse')

# Change secret key
SECRET_KEY = 'hmj#)-$smzqk*=wuz9^a46rex30^$_j$rghp+1#y&amp;i+pys5b@$'

# DEBUG mode: leave the hash in place for now, but remove it and the space (so DEBUG
# is at the start of the line) as soon as something doesn't work. Put it back
# when you get it working again.
# DEBUG = True

ALLOWED_HOSTS = [
    # Add the names and IP address of your host, for example:
    'openrem-server',
    'openrem-server.ad.abc.nhs.uk',
    '10.123.213.22',
]

LOG_ROOT = '/var/dose/log'
LOG_FILENAME = os.path.join(LOG_ROOT, 'openrem.log')
QR_FILENAME = os.path.join(LOG_ROOT, 'openrem_qr.log')
EXTRACTOR_FILENAME = os.path.join(LOG_ROOT, 'openrem_extractor.log')

# Removed comment hashes to enable log file rotation:
LOGGING['handlers']['file']['class'] = 'logging.handlers.RotatingFileHandler'
LOGGING['handlers']['file']['maxBytes'] = 10 * 1024 * 1024  # 10*1024*1024 = 10 MB
LOGGING['handlers']['file']['backupCount'] = 5  # number of log files to keep before deleting the oldest one
LOGGING['handlers']['qr_file']['class'] = 'logging.handlers.RotatingFileHandler'
LOGGING['handlers']['qr_file']['maxBytes'] = 10 * 1024 * 1024  # 10*1024*1024 = 10 MB
LOGGING['handlers']['qr_file']['backupCount'] = 5  # number of log files to keep before deleting the oldest one
LOGGING['handlers']['extractor_file']['class'] = 'logging.handlers.RotatingFileHandler'
LOGGING['handlers']['extractor_file']['maxBytes'] = 10 * 1024 * 1024  # 10*1024*1024 = 10 MB
LOGGING['handlers']['extractor_file']['backupCount'] = 5  # number of log files to keep before deleting the oldest one

# Regionalisation settings
#   Date format for exporting data to Excel xlsx files.
#   Default in OpenREM is dd/mm/yyyy. Override it by uncommenting and customising below; a full list of codes is
#   available at https://msdn.microsoft.com/en-us/library/ee634398.aspx.
# XLSX_DATE = 'mm/dd/yyyy'
#   Local time zone for this installation. Choices can be found here:
#   http://en.wikipedia.org/wiki/List_of_tz_zones_by_name
#   although not all choices may be available on all operating systems.
#   In a Windows environment this must be set to your system time zone.
TIME_ZONE = 'Europe/London'
#   Language code for this installation. All choices can be found here:
#   http://www.i18nguy.com/unicode/language-identifiers.html
LANGUAGE_CODE = 'en-us'

DCMTK_PATH = '/usr/bin'
DCMCONV = os.path.join(DCMTK_PATH, 'dcmconv')
DCMMKDIR = os.path.join(DCMTK_PATH, 'dcmmkdir')
JAVA_EXE = '/usr/bin/java'
JAVA_OPTIONS = '-Xms256m -Xmx512m -Xss1m -cp'
PIXELMED_JAR = '/var/dose/pixelmed/pixelmed.jar'
PIXELMED_JAR_OPTIONS = '-Djava.awt.headless=true com.pixelmed.doseocr.OCR -'

# E-mail server settings - see https://docs.djangoproject.com/en/2.2/topics/email/
EMAIL_HOST = 'localhost'
EMAIL_PORT = 25
EMAIL_HOST_USER = ''
EMAIL_HOST_PASSWORD = ''
EMAIL_USE_TLS = 0         # Use 0 for False, 1 for True
EMAIL_USE_SSL = 0         # Use 0 for False, 1 for True
EMAIL_DOSE_ALERT_SENDER = 'your.alert@email.address'
EMAIL_OPENREM_URL = 'http://your.openrem.server'

IGNORE_DEVICE_OBSERVER_UID_FOR_THESE_MODELS = ['GE OEC Fluorostar']

Now create the database. Make sure you are still in the openrem python folder and the virtualenv is active — prompt will look like

(veopenrem3)username@hostname:/var/dose/veopenrem3/lib/python3.10/site-packages/openrem/$

Otherwise see Activate the virtualenv and navigate back to that folder.

Upgrading to a new server

If you are upgrading to a new Linux server, use these additional commands before continuing with those below:

$ mv remapp/migrations/0001_initial.py{.1-0-upgrade,}

Import the database - update the path to the database backup file you copied from the old server:

$ pg_restore --no-privileges --no-owner -U openremuser -d openremdb /path/to/pre-1-0-upgrade-dump.bak

Migrate the database:

$ python manage.py migrate --fake-initial

$ python manage.py migrate remapp --fake

$ python manage.py makemigrations remapp
$ python manage.py migrate
$ python manage.py loaddata openskin_safelist.json
$ python manage.py collectstatic --no-input --clear
$ python manage.py compilemessages
$ python manage.py createsuperuser

Webserver¶

Configure NGINX and Gunicorn¶

Copy in the OpenREM site config file

$ cd /var/dose/veopenrem3/lib/python3.10/site-packages/openrem/
$ sudo cp sample-config/openrem-server /etc/nginx/sites-available/openrem-server

Note

Content of NGINX config file:

server {
    listen 80;
    server_name openrem-server;

    location /static {
        alias /var/dose/static;
    }

    location / {
        proxy_pass http://unix:/tmp/openrem-server.socket;
        proxy_set_header Host $host;
        proxy_read_timeout 300s;
    }
}

Remove the default config and make ours active:

$ sudo rm /etc/nginx/sites-enabled/default

$ sudo ln -s /etc/nginx/sites-available/openrem-server /etc/nginx/sites-enabled/openrem-server

Copy the Gunicorn systemd service file into place:

$ cd /var/dose/veopenrem3/lib/python3.10/site-packages/openrem/
$ sudo cp sample-config/openrem-gunicorn.service /etc/systemd/system/openrem-gunicorn.service

Note

Content of systemd file:

[Unit]
Description=Gunicorn server for OpenREM

[Service]
Restart=on-failure
User=www-data
WorkingDirectory=/var/dose/veopenrem3/lib/python3.10/site-packages/openrem

ExecStart=/var/dose/veopenrem3/bin/gunicorn \
    --bind unix:/tmp/openrem-server.socket \
    openremproject.wsgi:application --timeout 300

[Install]
WantedBy=multi-user.target

Copy the task queue consumer systemd service file into place:

$ cd /var/dose/veopenrem3/lib/python3.10/site-packages/openrem/
$ sudo cp sample-config/openrem-consumer.service /etc/systemd/system/openrem-consumer.service

Note

Content of systemd file:

[Unit]
Description=Huey consumer for OpenREM

[Service]
Restart=on-failure
User=www-data
WorkingDirectory=/var/dose/veopenrem3/lib/python3.10/site-packages/openrem

ExecStart=/var/dose/veopenrem3/bin/python \
    manage.py run_huey

[Install]
WantedBy=multi-user.target

Load the new systemd configurations:

$ sudo systemctl daemon-reload

Set the new Gunicorn and consumer services to start on boot:

$ sudo systemctl enable openrem-gunicorn.service
$ sudo systemctl enable redis-server.service
$ sudo systemctl enable openrem-consumer.service

Start the Gunicorn and consumer services, and restart the NGINX service:

$ sudo -- sh -c 'systemctl start openrem-gunicorn.service && systemctl start redis-server.service && systemctl start openrem-consumer.service && systemctl restart nginx.service'

Test the webserver¶

You should now be able to browse to the OpenREM server from another PC.

You can check that NGINX and Gunicorn are running with the following two commands:

$ sudo systemctl status openrem-gunicorn.service

$ sudo systemctl status nginx.service

DICOM Store SCP¶

Copy the Lua file to the Orthanc folder. This will control how we process the incoming DICOM objects.

$ cd /var/dose/veopenrem3/lib/python3.10/site-packages/openrem/
$ cp sample-config/openrem_orthanc_config_linux.lua /var/dose/orthanc/

Edit the Orthanc Lua configuration options:

$ nano /var/dose/orthanc/openrem_orthanc_config_linux.lua

Set use_physics_filtering to true if you want Orthanc to keep physics test studies, and have it put them in the /var/dose/orthanc/physics/ folder. Set it to false to disable this feature. Add names or IDs to physics_to_keep as a comma separated list.

-- Set this to true if you want Orthanc to keep physics test studies, and have it
-- put them in the physics_to_keep_folder. Set it to false to disable this feature
local use_physics_filtering = true

-- A list to check against patient name and ID to see if the images should be kept.
-- Orthanc will put anything that matches this in the physics_to_keep_folder.
local physics_to_keep = {'physics'}

Lists of things to ignore. Orthanc will ignore anything matching the content of these comma separated lists; they will not be imported into OpenREM.

-- Lists of things to ignore. Orthanc will ignore anything matching the content of
-- these lists: they will not be imported into OpenREM.
local manufacturers_to_ignore = {'Faxitron X-Ray LLC', 'Gendex-KaVo'}
local model_names_to_ignore = {'CR 85', 'CR 75', 'CR 35', 'CR 25', 'ADC_5146', 'CR975'}
local station_names_to_ignore = {'CR85 Main', 'CR75 Main'}
local software_versions_to_ignore = {'VixWin Platinum v3.3'}
local device_serial_numbers_to_ignore = {'SCB1312016'}

Enable or disable additional functionality to extract dose information from older Toshiba and GE scanners, and specify which CT scanners should use this method. Each system should be listed as {'Manufacturer', 'Model name'}, with systems in a comma separated list within curly brackets, as per the example below:

-- Set this to true if you want to use the OpenREM Toshiba CT extractor. Set it to
-- false to disable this feature.
local use_toshiba_ct_extractor = true

-- A list of CT make and model pairs that are known to have worked with the Toshiba CT extractor.
-- You can add to this list, but you will need to verify that the dose data created matches what you expect.
local toshiba_extractor_systems = {
        {'Toshiba', 'Aquilion'},
        {'GE Medical Systems', 'Discovery STE'},
}

Edit the Orthanc configuration:

$ sudo nano /etc/orthanc/orthanc.json

Add the Lua script to the Orthanc config:

// List of paths to the custom Lua scripts that are to be loaded
// into this instance of Orthanc
"LuaScripts" : [
"/var/dose/orthanc/openrem_orthanc_config_linux.lua"
],

Set the AE Title and port:

// The DICOM Application Entity Title
"DicomAet" : "OPENREM",

// The DICOM port
"DicomPort" : 104,

Note

Optionally, you may also like to enable the HTTP server interface for Orthanc (although if the Lua script is removing all the objects as soon as they are processed, you won’t see much!):

// Whether remote hosts can connect to the HTTP server
"RemoteAccessAllowed" : true,

// Whether or not the password protection is enabled
"AuthenticationEnabled" : false,

To see the Orthanc web interface, go to http://openremserver:8042/ – of course change the server name to that of your server!

Allow Orthanc to use DICOM port¶

By default, Orthanc uses port 4242. If you wish to use a lower port, specifically the DICOM port of 104, you will need to give the Orthanc binary special permission to do so:

$ sudo setcap CAP_NET_BIND_SERVICE=+eip /usr/sbin/Orthanc

Finish off¶

Restart Orthanc:

$ sudo systemctl restart orthanc.service

New users, and quick access to physics folder¶

This is for new Linux users; for new OpenREM users, refer to Configure the settings

If you left local use_physics_filtering = true in the Orthanc configuration, you might like to give your colleagues a quick method of accessing the physics folder from their home folder. Then if they use a program like WinSCP it is easy to find and copy the QA images to another (Windows) computer on the network. WinSCP can also be run directly from a USB stick if you are unable to install software :-)

Add the new user (replace newusername as appropriate):

$ sudo adduser newusername

Then add the new user to the openrem group (again, replace the user name):

$ sudo adduser newusername openrem

Now add a ‘sym-link’ to the new users home directory (again, replace the user name):

$ sudo ln -sT /var/dose/orthanc/physics /home/newusername/physicsimages

The new user should now be able to get to the physics folder by clicking on the physicsimages link when they log in, and should be able to browse, copy and delete the zip files and folders.

Asciinema demo of this install¶

Link to asciinema demo of this install

Upgrading a native Linux install¶

These instructions assume a configuration similar to the ‘One page complete Ubuntu install’ provided with release 0.8.1 and later. If you are running an older distribution, consider upgrading the operating system or migrating the service to a new host. The test system for these upgrade instructions was upgraded from 18.04 to 20.04 and then 22.04 before the OpenREM upgrade was started. If you are using a different distribution or have set up your system differently, it might be better to start afresh following or adapting the Upgrading to a new Linux server docs instead.

If upgrading to a new host, follow the Upgrading to a new Linux server docs.

This release will run on Python 3.8 or 3.9, but Python 3.10 is recommended. If a different release of Python is being used, substitute 3.10 for that version where necessary below.

If you are upgrading OpenREM on a Linux server with limited internet access, go to the Offline installation or upgrade docs.

Upgrades from 0.9.1 or earlier should review Upgrade to OpenREM 0.10.0 from 0.7.3 or later first. Upgrading to 1.0 is only possible from 0.10.0.

Preparation¶

Back up the database - you will need the password for openremuser that will be in your local_settings.py file. You’ll need this file again later so open it in a different window:

$ less /var/dose/veopenrem/lib/python2.7/site-packages/openrem/openremproject/local_settings.py

Backup the database, in the main window:

$ pg_dump -U openremuser -d openremdb -F c -f pre-1-0-upgrade-dump.bak

Stop any Celery workers, Flower, RabbitMQ, Gunicorn, NGINX, and Orthanc (OpenREM service names will be reversed if they weren’t changed with the 0.9.1 upgrade):

$ sudo systemctl stop openrem-celery
$ sudo systemctl stop openrem-flower
$ sudo systemctl stop openrem-gunicorn
$ sudo systemctl stop rabbitmq-server
$ sudo systemctl stop nginx
$ sudo systemctl stop orthanc

Update apt and install any updates:

$ sudo -- sh -c 'apt update && apt upgrade'

Install Python 3.10 and other packages:

$ sudo apt install acl python3.10 python3.10-dev python3.10-distutils python3.10-venv python3-pip \
postgresql nginx orthanc dcmtk default-jre zip gettext

Reset the permissions for the /var/dose folder:

$ sudo chmod -R 775 /var/dose
$ sudo chown -R $USER:openrem /var/dose
$ sudo chmod -R g+s /var/dose/*

Now find the uid of your user and the gid of the openrem group:

$ id
$ getent group openrem

Take note of the uid number and the gid in the third field of the group information and use it in the next command, replacing 1001 (user uid) and 1002 (openrem group gid) as appropriate:

$ sudo setfacl -PRdm u:1001:rwx,g:1002:rwx,o::r /var/dose/

What are we doing with the permissions?

These settings enable the web server user www-data, the DICOM server user orthanc and the OpenREM server users (you and your colleagues) to all read, write and execute the OpenREM files. The setfacl command relies on Access Control Lists being available on your system - they are usually enabled on ext4 and can be enabled on others. See New users, and quick access to physics folder for adding colleagues access to the Linux folders.

Create a new Python virtual environment:

$ python3.10 -m venv /var/dose/veopenrem3

Activate the virtualenv:

$ . /var/dose/veopenrem3/bin/activate

Install the new version of OpenREM¶

Ensure the new virtualenv is active — prompt will look like

(veopenrem3)username@hostname:~$

Upgrade Pip and install OpenREM

$ pip install --upgrade pip

$ pip install openrem==1.0.0b1

Configure the local_settings.py file¶

Navigate to the Python openrem folder and copy the example local_settings.py and wsgi.py files to remove the .linux and .example suffixes:

$ cd /var/dose/veopenrem3/lib/python3.10/site-packages/openrem/
$ cp openremproject/local_settings.py{.linux,}
$ cp openremproject/wsgi.py{.example,}

Review the old local_settings.py file that was opened earlier - see the first part of the Preparation section. Edit the new local_settings.py as needed - make sure you update the database NAME, USER and PASSWORD, the ALLOWED_HOSTS list and the EMAIL configuration and check all the other settings. Change the SECRET_KEY from the default:

$ nano openremproject/local_settings.py

DATABASES = {
    'default': {
        'ENGINE': 'django.db.backends.postgresql',
        'NAME': 'openremdb',
        'USER': 'openremuser',
        'PASSWORD': 'mysecretpassword',     # This is the password you set earlier
        'HOST': '',
        'PORT': '',
    }
}

MEDIA_ROOT = '/var/dose/media/'

STATIC_ROOT = '/var/dose/static/'

# Change secret key
SECRET_KEY = 'hmj#)-$smzqk*=wuz9^a46rex30^$_j$rghp+1#y&amp;i+pys5b@$'

# DEBUG mode: leave the hash in place for now, but remove it and the space (so DEBUG
# is at the start of the line) as soon as something doesn't work. Put it back
# when you get it working again.
# DEBUG = True

ALLOWED_HOSTS = [
    # Add the names and IP address of your host, for example:
    'openrem-server',
    'openrem-server.ad.abc.nhs.uk',
    '10.123.213.22',
]

LOG_ROOT = '/var/dose/log'
LOG_FILENAME = os.path.join(LOG_ROOT, 'openrem.log')
QR_FILENAME = os.path.join(LOG_ROOT, 'openrem_qr.log')
EXTRACTOR_FILENAME = os.path.join(LOG_ROOT, 'openrem_extractor.log')

# Removed comment hashes to enable log file rotation:
LOGGING['handlers']['file']['class'] = 'logging.handlers.RotatingFileHandler'
LOGGING['handlers']['file']['maxBytes'] = 10 * 1024 * 1024  # 10*1024*1024 = 10 MB
LOGGING['handlers']['file']['backupCount'] = 5  # number of log files to keep before deleting the oldest one
LOGGING['handlers']['qr_file']['class'] = 'logging.handlers.RotatingFileHandler'
LOGGING['handlers']['qr_file']['maxBytes'] = 10 * 1024 * 1024  # 10*1024*1024 = 10 MB
LOGGING['handlers']['qr_file']['backupCount'] = 5  # number of log files to keep before deleting the oldest one
LOGGING['handlers']['extractor_file']['class'] = 'logging.handlers.RotatingFileHandler'
LOGGING['handlers']['extractor_file']['maxBytes'] = 10 * 1024 * 1024  # 10*1024*1024 = 10 MB
LOGGING['handlers']['extractor_file']['backupCount'] = 5  # number of log files to keep before deleting the oldest one

# Regionalisation settings
#   Date format for exporting data to Excel xlsx files.
#   Default in OpenREM is dd/mm/yyyy. Override it by uncommenting and customising below; a full list of codes is
#   available at https://msdn.microsoft.com/en-us/library/ee634398.aspx.
# XLSX_DATE = 'mm/dd/yyyy'
#   Local time zone for this installation. Choices can be found here:
#   http://en.wikipedia.org/wiki/List_of_tz_zones_by_name
#   although not all choices may be available on all operating systems.
#   In a Windows environment this must be set to your system time zone.
TIME_ZONE = 'Europe/London'
#   Language code for this installation. All choices can be found here:
#   http://www.i18nguy.com/unicode/language-identifiers.html
LANGUAGE_CODE = 'en-us'

DCMTK_PATH = '/usr/bin'
DCMCONV = os.path.join(DCMTK_PATH, 'dcmconv')
DCMMKDIR = os.path.join(DCMTK_PATH, 'dcmmkdir')
JAVA_EXE = '/usr/bin/java'
JAVA_OPTIONS = '-Xms256m -Xmx512m -Xss1m -cp'
PIXELMED_JAR = '/var/dose/pixelmed/pixelmed.jar'
PIXELMED_JAR_OPTIONS = '-Djava.awt.headless=true com.pixelmed.doseocr.OCR -'

# E-mail server settings - see https://docs.djangoproject.com/en/2.2/topics/email/
EMAIL_HOST = 'localhost'
EMAIL_PORT = 25
EMAIL_HOST_USER = ''
EMAIL_HOST_PASSWORD = ''
EMAIL_USE_TLS = 0         # Use 0 for False, 1 for True
EMAIL_USE_SSL = 0         # Use 0 for False, 1 for True
EMAIL_DOSE_ALERT_SENDER = 'your.alert@email.address'
EMAIL_OPENREM_URL = 'http://your.openrem.server'

Migrate the database¶

In a shell/command window, move into the openrem folder:

$ cd /var/dose/veopenrem3/lib/python3.10/site-packages/openrem/

Prepare the migrations folder:

Rename 0001_initial.py.1-0-upgrade to 0001_initial.py

$ mv remapp/migrations/0001_initial.py{.1-0-upgrade,}

Migrate the database:

$ python manage.py migrate --fake-initial

$ python manage.py migrate remapp --fake

$ python manage.py makemigrations remapp

Rename questions

There will be some questions about fields being renamed - answer N to all of them.

$ python manage.py migrate

$ python manage.py loaddata openskin_safelist.json

Update static files and translations¶

$ python manage.py collectstatic --clear

Warning about deleting all files

You will get a warning about all files in the static files location being deleted. As long as the folder is correct, type yes to continue.

Virtual directory users

If you are running your website in a virtual directory, you also have to update the reverse.js file. To get the file in the correct path, take care that you insert just after the declaration of STATIC_ROOT the following line in your local_settings.py (see also the sample local_settings.py.example):

JS_REVERSE_OUTPUT_PATH = os.path.join(STATIC_ROOT, 'js', 'django_reverse')

To update the reverse.js file execute the following command:

$ python manage.py collectstatic_js_reverse

See Running the OpenREM website in a virtual directory for more details.

Generate translation binary files

$ python manage.py compilemessages

Update all the services configurations¶

Edit the Gunicorn systemd file WorkingDirectory and ExecStart:

$ sudo nano /etc/systemd/system/openrem-gunicorn.service

WorkingDirectory=/var/dose/veopenrem3/lib/python3.10/site-packages/openrem

ExecStart=/var/dose/veopenrem3/bin/gunicorn \
    --bind unix:/tmp/openrem-server.socket \
    openremproject.wsgi:application --timeout 300 --workers 4

Celery, Flower and RabbitMQ are no longer required for this release, so their Systemd control files can be disabled, and RabbitMQ can be removed (assuming it is not in use for any other services on this server):

$ sudo systemctl disable openrem-celery.service
$ sudo systemctl disable openrem-flower.service

$ sudo apt remove rabbitmq-server
$ sudo apt purge rabbitmq-server

Reload systemd and restart the services¶

$ sudo systemctl daemon-reload

Start and check Gunicorn:

$ sudo systemctl start openrem-gunicorn.service
$ sudo systemctl status openrem-gunicorn.service

Start and check NGINX:

$ sudo systemctl start nginx.service
$ sudo systemctl status nginx.service

Start and check Orthanc:

$ sudo systemctl start orthanc.service
$ sudo systemctl status orthanc.service

Registered Users error

If Orthanc fails to start, check the Orthanc log file:

$ sudo less /var/log/orthanc/Orthanc.log

If there is an error: Bad file format: The configuration section "RegisteredUsers" is defined in 2 different configuration files this might be due to changes in the installed version of Orthanc.

Edit the main Orthanc configuration file to remove the setting, as it is now in a credentials.json configuration file.

$ sudo nano /etc/orthanc/orthanc.json

Remove the RegisteredUsers setting and try again:

$ sudo systemctl start orthanc.service
$ sudo systemctl status orthanc.service

If there is still an issue, check the log again. If the problem this time is due to the TCP port of the DICOM server, you might need to give it permission again:

$ sudo setcap CAP_NET_BIND_SERVICE=+eip /usr/sbin/Orthanc

And restart Orthanc once more.

Test the webserver¶

You should now be able to browse to the web interface of your upgraded OpenREM system and have a look around.

Update the DICOM Store settings¶

Log in to the web interface, and navigate to Config, DICOM networking.

The remote nodes should be correct from the old system, but the DICOM Store SCP settings will need updating. Modify the store, and add the hostname localhost.

After you have clicked Submit, the status page should show the server is alive. If it isn’t, go and check the status of Orthanc again (we may have checked it too quickly before).

Upgrading to a new Linux server¶

If OpenREM has been running on an older Linux distribution, or you wish to move to Linux to host OpenREM and don’t want to use Docker, these instructions will guide you through upgrading an existing database to a new server.

Upgrades from 0.9.1 or earlier should review Upgrade to OpenREM 0.10.0 from 0.7.3 or later first. Upgrading to 1.0 is only possible from 0.10.0.

This install is based on Ubuntu 22.04 using:

Python 3.10 running in a virtualenv
Database: PostgreSQL
DICOM Store SCP: Orthanc running on port 104
Webserver: NGINX with Gunicorn
All OpenREM files in /var/dose/ with group owner of openrem
Collects any Physics (QA) images and zips them

Get the local_settings.py file¶

Get local_settings.py file from the old server - it should be in one of these locations:

Ubuntu ‘One page install’: /var/dose/veopenrem/lib/python2.7/site-packages/openrem/openremproject/local_settings.py
Ubuntu linux: /usr/local/lib/python2.7/dist-packages/openrem/openremproject/local_settings.py
Other linux: /usr/lib/python2.7/site-packages/openrem/openremproject/local_settings.py
Linux virtualenv: vitualenvfolder/lib/python2.7/site-packages/openrem/openremproject/local_settings.py
Windows: C:\Python27\Lib\site-packages\openrem\openremproject\local_settings.py
Windows virtualenv: virtualenvfolder\Lib\site-packages\openrem\openremproject\local_settings.py

Export the database¶

Export the old database on the old server - you will need the password for openremuser that will be in your local_settings.py file, and you might need to change the openremuser database username and the openremdb name of the database:

$ pg_dump -U openremuser -d openremdb -F c -f pre-1-0-upgrade-dump.bak

Transfer the files¶

Copy these two files to your new server.

Continue on the new server¶

Now follow the Native Linux install instructions looking out for the additional steps for upgrading to a new Linux server.

Offline installation or upgrade¶

In order to install or upgrade OpenREM on a Windows server that does not have access to the internet you will need to download all the packages and dependencies on another computer and copy them across.

If you have trouble when installing the Python packages on Windows due to incorrect architecture, you may need to either download on a Windows system similar to the server (matching 32-bit/64-bit), or to download the files from http://www.lfd.uci.edu/~gohlke/pythonlibs/ instead. Alternatively there are ways to tell pip to download binary packages for specific platforms.

It is expected and highly recommended that server operating systems have access to security updates even when other internet access is blocked.

The instructions that follow are for a Windows server that doesn’t have access to the internet. For Linux servers, it is recommended to allow access to the distribution’s repositories to install and update the software. It is technically possible to use a local repository mirror/cache, or to download all the packages manually, but this is beyond the scope of these instructions.

An Offline Docker installation might be easier on an offline Linux server, once Docker and Docker Compose are installed.

On a computer with internet access¶

Download independent binaries¶

Download all the software in the Installing packages section except IIS:

Python
Orthanc
PostgreSQL
gettext
Pixelmed
dcmtk
7Zip
Notepad++
WinSW

Download Python packages from PyPI¶

In a console, navigate to a suitable place and create an empty directory to collect all the packages in, then use pip to download them all - Python 3 (including Pip) will need to be installed on the computer with internet access to download the packages, ideally Python 3.10:

C:\Users\me\Desktop> mkdir openremfiles
C:\Users\me\Desktop> pip3 download -d openremfiles pip
C:\Users\me\Desktop> pip3 download -d openremfiles openrem==1.0.0b2
C:\Users\me\Desktop> pip3 download -d openremfiles wfastcgi

Copy everything to the Server¶

Copy this directory plus the binaries to the offline server.

On the server without internet access¶

Follow the Native Windows install, Upgrading to a new Windows server, or Upgrading a native Windows install instructions, installing the binary packages that were copied across as well as IIS. The Install OpenREM section has instructions on how to install OpenREM python packages from the folder you have copied across.

Native install on Windows¶

A native installation on Windows Server requires Python, a database (ideally PostgreSQL) and a DICOM server (ideally Orthanc) to be installed, with OpenREM and all the other dependencies being installed via Pip. IIS is the recommended webserver to use on Windows.

This installation process can be used with Windows 10 (and probably 11), but this is not advised for production use as Windows 10 and 11 are not designed to be servers.

As for native Linux installs, existing installations of OpenREM 0.10 can be upgraded, but this release requires a different version of Python to the older releases, and some services that were previously required are no longer needed. Full upgrade instructions are provided, based on a Windows Server 2019 installation.

Native Windows install¶

Document not ready for translation

This install is based on Windows Server 2022 using:

Python 3.10 running in a virtualenv
Database: PostgreSQL
DICOM Store SCP: Orthanc running on port 104
Webserver: Microsoft IIS running on port 80
WinSW to run background tasks as services
Notepad++ for editing files
Database files stored on D:
OpenREM files stored on E:
With Physics (QA) images being collected and zipped for retrieval

The instructions should work for Windows Server 2016 and 2019; and will probably work with Windows 10/11 with some modification. Desktop editions of Windows are not recommended for a production OpenREM install.

If you are upgrading an existing installation to a new Windows server, go to the Upgrading to a new Windows server first.

If you are upgrading an existing Windows Server installation in-place, go to Upgrading a native Windows install instead.

If you are installing on a server with no internet access, go to Offline installation or upgrade to download the packages.

These instructions assume the following disk layout - there is more information about the reasoning in the box below:

C: OS disk
D: Database disk
E: Data disk

Initial prep¶

Creating folders¶

Why D: and E: drives?

OpenREM data are stored on drive E: to keep the data away from the operating system drive so that it is easier for building/recreating the server and knowing what needs to be backed up.

For the same reason, we will install PostgreSQL so that the database data are store on drive D: - this makes it possible to provide a different configuration of disk for the database drive, with different backup policies.

However, it is also possible to store all the data on the C: drive if that works better for your installation. In this case, it would be advisable to create a folder C:\OpenREM\ and create all the folders specified below into that folder.

You can also use different drive letters if that works better for your installation. In both cases paths will need to be modified in the instructions to suite.

Figure 1: Windows install folder layout¶

Create the following folders. The instructions here are for a CMD window but they can be created in Windows Explorer instead:

C:\Users\openrem>D:
D:\>mkdir database
D:\>E:
E:\>mkdir log media pixelmed dcmtk 7-zip static task_queue venv orthanc\dicom orthanc\physics orthanc\storage winsw

Set permissions¶

Right click on the E:\log folder and click Properties
In the Security tab click Edit... and Add...

If the server is connected to a domain

If the server is connected to a domain, the From this location: will have the name of the domain. Click Locations... and choose the name of the server instead of the domain name.

Enter the object name IIS_IUSRS and click OK
Tick the Modify Allow to enable read and write permissions
Click OK twice to close the dialogues
Repeat for the E:\media and E:\task_queue folders

Installing packages¶

Python¶

Download the latest version for Windows from https://www.python.org/downloads/ as long as it is in the 3.10 series. OpenREM v1.0 has not been tested with Python 3.11 yet.

Open the downloaded file to start the installation:

Customize installation
Leave all the Optional Features ticked, and click Next
Tick Install for all users - this will automatically tick Precompile standard library
Install
Click to Disable path length limit - might not be necessary but might be useful!
Close

Orthanc¶

Download the 64 bit version from https://www.orthanc-server.com/download-windows.php.

The download file might be blocked because it isn’t a commonly downloaded executable. Click the ... menu and select Keep. Then click Show more and Keep anyway.

Open the downloaded file to start the installation:

Click Next >, accept the agreement and Next > again.
Default install location, Next >
Select Orthanc storage directory - Browse... to E:\orthanc\storage, OK and Next >
Click Next > for a Full installation
Start Menu Folder Next >
Ready to Install Install
Finish

PostgreSQL¶

Download the latest version of PostgreSQL from https://www.enterprisedb.com/downloads/postgres-postgresql-downloads - choose the Windows x86-64 version. OpenREM v1.0 has been tested with PostgreSQL v14.5.

Open the downloaded file to start the installation:

Some Microsoft redistributables will install
Click Next > to start
Default Installation Directory Next >
All components Next >
Data Directory - browse to D:\database then Select folder and Next >
Create a password for the postgres superuser - you will need this to setup the database with pgAdmin 4 later
Enter it twice and Next >
Default port Next >
Default Locale Next >
Pre Installation Summary Next >
Ready to Install Next > and the installation will begin
Untick Launch Stack Builder at exit
Finish

gettext¶

Download the 64 bit static version of gettext 0.21 from https://mlocati.github.io/articles/gettext-iconv-windows.html. Use the .exe version (software install icon, not the zip icon)

Open the downloaded file to start the installation:

Accept the agreement Next >
Default installation directory Next >
Additional Tasks leave both boxes ticked Next >
Ready to Install Install
Finish

What is gettext for?

The gettext binary enables the translations to be available to users of the web interface. It is not essential if you don’t want the translations to be available.

Pixelmed¶

Download DoseUtility from from the page http://www.dclunie.com/pixelmed/software/webstart/DoseUtilityUsage.html - find How to install it (locally) near the bottom of the page and click the Windows executable that does not require Java to be installed link.

Open the downloaded zip file and open a new file browser at E:\pixelmed
Drag the contents of the zip file to the pixelmed folder

DCMTK¶

Download from https://dcmtk.org/dcmtk.php.en - look for the DCMTK executable binaries section, and download the 64 bit DLL build for Windows.

Open the downloaded zip file and open a new file browser at E:\dcmtk
Drag the contents of the dcmtk-3.x.x-win64-dynamic folder in the zip file to the dcmtk folder
You should end up with E:\dcmtk\bin\ etc

7Zip¶

Download the 64-bit x64 exe file from https://www.7-zip.org/

Type, or click on the ... to browse to E:\7-zip\
Install
Close

WinSW¶

Download the 64-bit x64 exe file from https://github.com/winsw/winsw/releases/tag/v2.12.0

Open a new file browser at E:\winsw
Drag the exe file to the winsw folder
Rename the exe file from WinSW-x64 to WinSW

Notepad++¶

Download the latest version of Notepad++ from https://notepad-plus-plus.org/downloads/

Open the downloaded file to start the installation:

Select a language OK
Welcome Next >
License Agreement I Agree
Install Location Next >
Choose Components Next >
Install
Finish (you can untick the Run Notepad++ option, we don’t need it yet)

IIS¶

Open the Control Panel
Search for windows features
Select Turn Windows features on or off
Start the wizard Next >
Role-based or feature-based installation Next >
Leave the current server highlighted Next >
Check the Web Server (IIS) box
In the pop-up dialogue for adding IIS Management Console, click Add Features
Next >
Features, Next >
Web Server Role (IIS) Next >
Expand the Application Development section
Check the CGI box, Next >
Install
Close

You can check the server is running by browsing to http://localhost/ on the server. You should see the default IIS Welcome page. It might not work immediately, check again in a few minutes.

Installing Python packages¶

Create and activate the virtualenv¶

Open a CMD window:

C:\Users\openrem>e:
E:\>py -m venv venv
E:\>venv\Scripts\activate
(venv) E:\>

Install OpenREM¶

Installing on a server with no internet access

Make sure the virtualenv is activated (command line will have the name of the virtualenv as a prefix: (venv) E:\), then navigate to where the openremfiles directory is that you copied from the computer with internet access, eg if it is in your desktop folder:

(venv) E:\>c:
(venv) C:\>cd Users\openrem\Desktop

Now upgrade pip and install OpenREM and its dependencies:

(venv) C:\Users\openrem\Desktop>pip install --no-index --find-links=openremfiles --upgrade pip
(venv) C:\Users\openrem\Desktop>pip install --no-index --find-links=openremfiles openrem

(venv) E:\>pip install --upgrade pip
(venv) E:\>pip install openrem==1.0.0b2
(venv) E:\>pip install wfastcgi

OpenREM configuration and database creation¶

PostgreSQL database creation¶

Start pgAdmin 4 - you will need the password you set when installing PostgreSQL

Create user¶

Click on Servers to expand, enter the password again
Right click Login/Group Roles, Create, Login/Group Role...
Name: openremuser
Definition, Password: add a password for the openremuser
Privileges: activate Can login? and Create database?
Save

Create database¶

Right click Databases, Create, Database...
Database: openremdb
Owner: openremuser
Save

Configure OpenREM¶

Open the E:\venv\Lib\site-packages\openrem\openremproject folder and rename the example local_settings.py and wsgi.py files to remove the .windows and .example suffixes. Removing the file name extension will produce a warning to check if you are sure - Yes:

Edit local_settings.py as needed (right click Edit with Notepad++) Make sure you change the PASSWORD, the SECRET_KEY (to anything, just change it), the ALLOWED_HOSTS list, regionalisation settings and the EMAIL configuration. You can modify the email settings later if necessary. Some settings are not shown here but are documented in the settings file or elsewhere in the docs. For details on the final variable see Systems where Device Observer UID is not static.

Upgrading to a new server

If you are upgrading to a new Linux server, review the local_settings.py file from the old server to copy over the ALLOWED_HOSTS list and the EMAIL configuration, and check all the other settings. Change the SECRET_KEY from the default, but it doesn’t have to match the one on the old server. The database NAME, USER and PASSWORD will be the ones you created on the new server. For details on the final variable see Systems where Device Observer UID is not static.

DATABASES = {
    'default': {
        'ENGINE': 'django.db.backends.postgresql',  # Add 'postgresql', 'mysql', 'sqlite3' or 'oracle'.
        'NAME': 'openremdb',                     # Or path to database file if using sqlite3.
        'USER': 'openremuser',                   # Not used with sqlite3.
        'PASSWORD': '',                          # Not used with sqlite3.
        'HOST': '',                              # Set to empty string for localhost. Not used with sqlite3.
        'PORT': '',                              # Set to empty string for default. Not used with sqlite3.
    }
}

TASK_QUEUE_ROOT = 'E:/task_queue/'

MEDIA_ROOT = 'E:/media/'

STATIC_ROOT = 'E:/static/'
JS_REVERSE_OUTPUT_PATH = os.path.join(STATIC_ROOT, 'js', 'django_reverse')

# Change secret key
SECRET_KEY = 'hmj#)-$smzqk*=wuz9^a46rex30^$_j$rghp+1#y&amp;i+pys5b@$'

# DEBUG mode: leave the hash in place for now, but remove it and the space (so DEBUG
# is at the start of the line) as soon as something doesn't work. Put it back
# when you get it working again.
# DEBUG = True

ALLOWED_HOSTS = [
    # Add the names and IP address of your host, for example:
    'openrem-server',
    'openrem-server.ad.abc.nhs.uk',
    '10.123.213.22',
]

LOG_ROOT = 'E:/log/'
LOG_FILENAME = os.path.join(LOG_ROOT, 'openrem.log')
QR_FILENAME = os.path.join(LOG_ROOT, 'openrem_qr.log')
EXTRACTOR_FILENAME = os.path.join(LOG_ROOT, 'openrem_extractor.log')

# Regionalisation settings
#   Date format for exporting data to Excel xlsx files.
#   Default in OpenREM is dd/mm/yyyy. Override it by uncommenting and customising below; a full list of codes is
#   available at https://msdn.microsoft.com/en-us/library/ee634398.aspx.
# XLSX_DATE = 'mm/dd/yyyy'
#   Local time zone for this installation. Choices can be found here:
#   http://en.wikipedia.org/wiki/List_of_tz_zones_by_name
#   although not all choices may be available on all operating systems.
#   In a Windows environment this must be set to your system time zone.
TIME_ZONE = 'Europe/London'
#   Language code for this installation. All choices can be found here:
#   http://www.i18nguy.com/unicode/language-identifiers.html
LANGUAGE_CODE = 'en-us'

DCMTK_PATH = 'E:/dcmtk/bin'
DCMCONV = os.path.join(DCMTK_PATH, 'dcmconv.exe')
DCMMKDIR = os.path.join(DCMTK_PATH, 'dcmmkdir.exe')
JAVA_EXE = 'E:/pixelmed/windows/jre/bin/java.exe'
JAVA_OPTIONS = '-Xms256m -Xmx512m -Xss1m -cp'
PIXELMED_JAR = 'E:/pixelmed/pixelmed.jar'
PIXELMED_JAR_OPTIONS = '-Djava.awt.headless=true com.pixelmed.doseocr.OCR -'

# E-mail server settings - see https://docs.djangoproject.com/en/2.2/topics/email/
EMAIL_HOST = 'localhost'
EMAIL_PORT = 25
EMAIL_HOST_USER = ''
EMAIL_HOST_PASSWORD = ''
EMAIL_USE_TLS = 0         # Use 0 for False, 1 for True
EMAIL_USE_SSL = 0         # Use 0 for False, 1 for True
EMAIL_DOSE_ALERT_SENDER = 'your.alert@email.address'
EMAIL_OPENREM_URL = 'http://your.openrem.server'

IGNORE_DEVICE_OBSERVER_UID_FOR_THESE_MODELS = ['GE OEC Fluorostar']

Populate OpenREM database and collate static files¶

In a CMD window, move to the openrem Python folder and activate the virtualenv:

C:\Users\openrem>e:
E:\>cd venv\Lib\site-packages\openrem
E:\venv\Lib\site-packages\openrem>e:\venv\Scripts\activate
(venv) E:\venv\Lib\site-packages\openrem>

Upgrading to a new server

If you are upgrading to a new Windows server, do these additional steps before continuing with those below:

Rename E:\venv\Lib\site-packages\openrem\remapp\migrations\0001_initial.py.1-0-upgrade to 0001_initial.py

Import the database - update the path to the database backup file you copied from the old server. These steps can take a long time depending on the size of the database and the resources of the server:

C:\Users\openrem>"c:\Program Files\PostgreSQL\14\bin\pg_restore.exe" --no-privileges --no-owner -U openremuser -d openremdb -W windump.bak

Migrate the database:

(venv) E:\venv\Lib\site-packages\openrem>python manage.py migrate --fake-initial

(venv) E:\venv\Lib\site-packages\openrem>python manage.py migrate remapp --fake

(venv) E:\venv\Lib\site-packages\openrem>python manage.py makemigrations remapp

Warning

Make sure you didn’t get a RuntimeWarning when running the last command - scroll back up to the command and check you don’t see the following:

(venv) E:\venv\Lib\site-packages\openrem>python manage.py makemigrations remapp
E:\venv\lib\site-packages\django\core\management\commands\makemigrations.py:105: RuntimeWarning:

Got an error checking a consistent migration history performed for database connection 'default': unable to
open database file

If you do, check the database name and password settings in the local_settings.py file. You will need to delete the file E:\venv\Lib\site-packages\openrem\remapp\migrations\0001_initial.py before trying again.

(venv) E:\venv\Lib\site-packages\openrem>python manage.py migrate
(venv) E:\venv\Lib\site-packages\openrem>python manage.py loaddata openskin_safelist.json
(venv) E:\venv\Lib\site-packages\openrem>python manage.py collectstatic --no-input --clear

Create the translation files, assuming gettext was installed:

(venv) E:\venv\Lib\site-packages\openrem>python manage.py compilemessages

If this is a new install, not an upgrade, create the superuser account:

(venv) E:\venv\Lib\site-packages\openrem>python manage.py createsuperuser

Webserver¶

Configure IIS¶

Open Internet Information Services (IIS) Manager from the Start menu or the Administrative Tools.
Click on the name of your server in the Connections pane on the left
Double click on FastCGI Settings
In the Actions pane on the right, click Add Application
In the Full Path: box type or browse to E:\venv\Scripts\python.exe
In the Arguments box type the path to wfastcgi.py: E:\venv\Lib\site-packages\wfastcgi.py
Under FastCGI properties, click on (Collection) next to Environment Variables and click on the grey … box
In the EnvironmentVariables Collection Editor click Add
Change the value of Name to DJANGO_SETTINGS_MODULE (must be upper-case)
Set the Value to openremproject.settings
Click Add again and add the variable name PYTHONPATH with the value E:\venv\Lib\site-packages\openrem
Click Add again and add the variable name WSGI_HANDLER with the value django.core.wsgi.get_wsgi_application()
Click OK

Figure 7: Environment Variables Collection Editor¶

Under FastCGI Properties -> Process Model click on the Activity Timeout value and change it to 1200

Activity Timeout on slow running systems

If you encounter issues with long-running requests failing on slow running systems, you might try increasing the value of the Activity Timeout further.

Figure 8: Add FastCGI Application settings¶

Click OK to close the dialogue box

Create a new website¶

In the Connections pane expand the tree under server name
Expand the Sites folder, right click on Default Website and click Remove
Click Yes
Right click on Sites and click Add Website…
Enter Site name as OpenREM
Under Content Directory Physical path enter or browse to E:\venv\Lib\site-packages\openrem
Click OK

Configure the new website¶

Click on the OpenREM site under Connections in the left pane
Double click on Handler Mappings
In the right pane, under Actions click Add Module Mapping…
In the Request Path box enter an asterix (*)
In the Module box select FastCgiModule (not the CgiModule)
In the Executable box enter E:\venv\Scripts\python.exe|E:\venv\Lib\site-packages\wfastcgi.py
In Name type OpenREM CGI handler (value of name is not important)
Click Request Restrictions and untick the Invoke handler only if request is mapped to: checkbox
Click OK twice to close the Request Restrictions dialog and the Add Module Mapping dialogue
When prompted Do you want to create a FastCGI application for this executable? click No

Quick test!

You can now browse on the server to http://localhost/ and you should see an “ugly” version of the website. It will look better after we have configured the static files, next!

Configure IIS to server the static files¶

Right click on the OpenREM site under Connections in the left pane
Click Add Virtual Directory
Enter static as the Alias
Enter or browse to E:\static as the Physical path
Click OK
Double click on Handler Mappings in the middle pane
Click on View Ordered List... in the right pane
Select StaticFile
Click Move Up in the Action pane on the right until StaticFile is at the top
There will be a warning about the list order being changed - click Yes to continue

Test the webserver¶

Browse to http://localhost/ on the server, or browse to the servername in a browser on another machine, and you should be able to see the new OpenREM web service.

Task queue¶

Running OpenREM on Windows 10 or Windows 11?

For non-server environments, where task executors don’t need to be persistent across system restarts, there is a shortcut for starting workers. You can start a single worker in a new console as follows:

C:\Users\openrem>E:
E:\>cd venv\Lib\site-packages\openrem
E:\venv\Lib\site-packages\openrem>e:\venv\Scripts\activate
(venv) E:\venv\Lib\site-packages\openrem>python manage.py run_huey

If you want more than one worker to run tasks in parallel, you will need to repeat the previous steps for each additional worker in a new console.

You can stop a worker by pressing Ctrl + C in the appropriate console

If you cannot start a worker or you are getting error messages, please make sure that your current user has read and write permissions in the E:\task_queue directory.

OpenREM uses a task queue to run its background tasks. Therefore, we need additional Windows services that allow us to run these tasks separately from the web application.

To accomplish that we need to do the following:

Create local service account¶

First we need to create an account that will allow the IIS worker to control the task workers. Most importantly, to kill a task if necessary.

There is a difference if you are connected to an Active Directory or not. Whatever suits your setup, follow the guide A if you are not in an Active Directory or B if you are.

Guide A¶

For a Windows instance which is not associated to an Active Directory, it suffices to create a local user account:

Open the Search Tab
Search for Add, edit, or remove other users
In the menu, click Add someone else to this PC
In the left pane right click on Users
Click New User...
Fill in all fields with the data of a new user account (see image)
Untick User must change password at next login
Click Create
In the left pane click on Groups
Right click on IIS_IUSRS
Click Add to Group...
Click on the Add button
In the textfield, enter the username of the previously created account
Click Ok twice

Guide B¶

For a Windows instance that is connected to an Active Directory, or even a controller of one, follow this guide:

Open the Server Manager
In the navigation bar, click on Tools
Click Active Directory Users and Computers
In the left pane, expand your domain
Right click on Users
Hove over New
Click on User
Fill in all required fields with the data of a new user account
Click Next
Enter the new user password twice and untick User must change password at next login
Click Next and then Finish to create the service account

Creating worker services¶

Copy the file from

E:\venv\Lib\site-packages\openrem\sample-config\queue-init.bat to
E:\winsw\

Make sure that the previously downloaded and renamed WinSW.exe file is in the same folder (E:\winsw\).

Double click the queue-init.bat file
Enter your Domain name or leave empty if not applicable
Enter the username of the previously created account
Enter the associated password
Enter the number of workers you would like to spawn, this number should no exceed the number of CPU cores available to your system
Wait for the services to get registered and started up (Notice: many windows may appear and disappear quickly)

Adjusting IIS Application Pool Identity¶

Open Internet Information Services (IIS) Manager from the Start menu or the Administrative Tools.
In the Connections pane expand the tree under server name
Click on Application Pools
Right click on OpenREM in the middle pane
Click Advanced Settings...
Under Process Model click on Identity and then on the grey … box
Select the Custom account: radio button
Click on Set...
Enter the credentials of the preivously created account. If you are in an Active Directory prefix ther usernmae with <YOUR-DOMAIN>\
Click OK three times

DICOM Store SCP¶

Copy the Lua file to the Orthanc folder. This will control how we process the incoming DICOM objects.

Copy the file from

E:\venv\Lib\site-packages\openrem\sample-config\openrem_orthanc_config_windows.lua to
E:\orthanc\

Edit the Orthanc Lua configuration options - right click on the file you just copied Edit with Notepad++

Set use_physics_filtering to true if you want Orthanc to keep physics test studies, and have it put them in the E:\orthanc\dicom\ folder. Set it to false to disable this feature. Add names or IDs to physics_to_keep as a comma separated list.

-- Set this to true if you want Orthanc to keep physics test studies, and have it
-- put them in the physics_to_keep_folder. Set it to false to disable this feature
local use_physics_filtering = true

-- A list to check against patient name and ID to see if the images should be kept.
-- Orthanc will put anything that matches this in the physics_to_keep_folder.
local physics_to_keep = {'physics'}

Lists of things to ignore. Orthanc will ignore anything matching the content of these comma separated lists; they will not be imported into OpenREM.

-- Lists of things to ignore. Orthanc will ignore anything matching the content of
-- these lists: they will not be imported into OpenREM.
local manufacturers_to_ignore = {'Faxitron X-Ray LLC', 'Gendex-KaVo'}
local model_names_to_ignore = {'CR 85', 'CR 75', 'CR 35', 'CR 25', 'ADC_5146', 'CR975'}
local station_names_to_ignore = {'CR85 Main', 'CR75 Main'}
local software_versions_to_ignore = {'VixWin Platinum v3.3'}
local device_serial_numbers_to_ignore = {'SCB1312016'}

Enable or disable additional functionality to extract dose information from older Toshiba and GE scanners, and specify which CT scanners should use this method. Each system should be listed as {'Manufacturer', 'Model name'}, with systems in a comma separated list within curly brackets, as per the example below:

-- Set this to true if you want to use the OpenREM Toshiba CT extractor. Set it to
-- false to disable this feature.
local use_toshiba_ct_extractor = true

-- A list of CT make and model pairs that are known to have worked with the Toshiba CT extractor.
-- You can add to this list, but you will need to verify that the dose data created matches what you expect.
local toshiba_extractor_systems = {
        {'Toshiba', 'Aquilion'},
        {'GE Medical Systems', 'Discovery STE'},
}

Save any changes.

Edit the Orthanc configuration. Navigate to C:\Program Files\Orthanc Server\Configuration and right click on orthanc.json and click Edit with Notepad++:

Add the Lua script to the Orthanc config:

// List of paths to the custom Lua scripts that are to be loaded
// into this instance of Orthanc
"LuaScripts" : [
"E:\\orthanc\\openrem_orthanc_config_windows.lua"
],

Set the AE Title and port:

// The DICOM Application Entity Title
"DicomAet" : "OPENREM",

// The DICOM port
"DicomPort" : 104,

Note

Optionally, you may also like to enable the HTTP server interface for Orthanc (although if the Lua script is removing all the objects as soon as they are processed, you won’t see much!):

// Whether remote hosts can connect to the HTTP server
"RemoteAccessAllowed" : true,

// Whether or not the password protection is enabled
"AuthenticationEnabled" : false,

You will also need to open the firewall for port 8042.

To see the Orthanc web interface, go to http://openremserver:8042/ – of course change the server name to that of your server!

Save any changes.

Allow DICOM traffic through the firewall¶

Type windows firewall in the Start menu to open Windows Defender Firewall
Click Advanced settings in the left hand pane to open Windows Defender Firewall with Advanced Security
Click Inbound Rules in the left hand pane
Click New Rule... in the right hand pane
Click Port and Next >
Leave as TCP and specify port 104 and click Next >
Allow the connection, Next >
Leave the boxes ticked for When does this rule apply if that is appropriate, Next >
Name Orthanc DICOM port
Finish

Finish off¶

Restart Orthanc:

Launch Services from the start menu
Find Orthanc on the list and click Restart
Orthanc logs can be reviewed at C:\Program Files\Orthanc Server\Logs - the current log file will have the latest date and time in the filename - right click Edit with Notepad++

You can check if the port is running and allowed through the firewall using the Network tab of Resource Monitor.

Upgrading a native Windows install¶

Release 1.0 of OpenREM uses a newer version of Python and no longer uses RabbitMQ, Erlang and Celery. Instructions are only provided for Orthanc DICOM server, and no longer for Conquest. The built-in DICOM Store node has been removed.

Consider upgrading to a new Windows server instead of upgrading in place. Instructions for Upgrading to a new Windows server are provided including exporting and importing the existing PostgreSQL database.

something about a clean install, and/or not having old services that are no longer required
something about being a standardised approach which will make upgrade docs and examples easier to follow

Upgrades from 0.9.1 or earlier should review Upgrade to OpenREM 0.10.0 from 0.7.3 or later first. Upgrading to 1.0 is only possible from 0.10.0.

Then best effort upgrade docs… a lot of this can be copied from the Native Windows install instructions, or depending on what it ends up looking like, we might point there with a few admonitions to point out differences?

Export database for backup
Stop all the services
Install Python 3.10
Update PostgreSQL, Orthanc, DCMTK, Pixelmed
Add/update as necessary gettext, 7Zip, Notepad++
Install IIS if Apache/NGINX previously in use
Create virtualenv, activate
Install new OpenREM, wfastcgi
Configure OpenREM - use new local_settings.py.windows, adjust database name etc
Will database be available in new version of PostgreSQL? Or does it need to be imported?
Rename 0001_initial.py file
Do the fake-initial etc stuff
Do the rest of the manage.py stuff
Configure/reconfigure IIS
Configure/reconfigure Orthanc

Upgrading to a new Windows server¶

If OpenREM has been running on an older Windows server version, or you wish to move to Windows Server to host OpenREM, these instructions will guide you through upgrading an existing database to a new server.

This install is based on Windows Server 2022 - for details see the main Native Windows install docs.

Upgrades from 0.9.1 or earlier should review Upgrade to OpenREM 0.10.0 from 0.7.3 or later first. Upgrading to 1.0 is only possible from 0.10.0.

Get the local_settings.py file¶

Get local_settings.py file from the old server - it should be in one of these locations:

Ubuntu ‘One page install’: /var/dose/veopenrem/lib/python2.7/site-packages/openrem/openremproject/local_settings.py
Ubuntu linux: /usr/local/lib/python2.7/dist-packages/openrem/openremproject/local_settings.py
Other linux: /usr/lib/python2.7/site-packages/openrem/openremproject/local_settings.py
Linux virtualenv: vitualenvfolder/lib/python2.7/site-packages/openrem/openremproject/local_settings.py
Windows: C:\Python27\Lib\site-packages\openrem\openremproject\local_settings.py
Windows virtualenv: virtualenvfolder\Lib\site-packages\openrem\openremproject\local_settings.py

Export the database¶

Export the old database on the old server - get details from the local_settings.py file:

Check the database username and change in the command below as necessary (openremuser)
Check the database name and change in the command below as necessary (openremdb)
You will need the password for openremuser
You will need to edit the command for the path to pg_dump.exe - the 14 is likely to be a lower number

C:\Users\openrem>"c:\Program Files\PostgreSQL\14\bin\pg_dump.exe" -U openremuser -d openremdb -F c -f windump.bak

Transfer the files¶

Copy these two files to your new server.

Continue on the new server¶

Now follow the Native Windows install instructions looking out for the additional steps for upgrading to a new server.

Databases¶

Database administration¶

Docker installations¶

Database backup¶

Open a shell (command prompt) in the Docker folder

$ docker-compose exec db pg_dump -U openremuser -d openrem_prod -F c -f /db_backup/openremdump.bak

To automate a regular backup (recommended) adapt the following command in a bash script:

#!/bin/bash
TODAY=$(date "+%Y-%m-%d")
docker-compose -f /path/to/docker-compose.yml exec db pg_dump -U openremuser -d openrem_prod -F c -f "/db_backup/openremdump-"$TODAY".bak"

or powershell script:

$dateString = "{0:yyyy-MM-dd}" -f (get-date)
docker-compose -f C:\Path\To\docker-compose.yml exec db pg_dump -U openremuser -d openrem_prod -F c -f /db_backup/openremdump-$dateString.bak

You will need to ensure the backups are either regularly deleted/moved, or overwritten so that the backups don’t fill the disk.

Database restore¶

To restore a database backup to a new Docker container, install using the Installation instructions and bring the containers up, but don’t run the database commands. These instructions can also be used to create a duplicate server on a different system for testing or other purposes.

Requires exactly the same version of OpenREM to be installed as the database was exported from
Copy the database backup to the db_backup/ folder of the new install (the name is assumed to be openremdump.bak, change as necessary)
Open a shell (command prompt) in the new install folder (where docker-compose.yml is)

$ docker-compose exec db pg_restore --no-privileges --no-owner -U openremuser -d openrem_prod /db_backup/openremdump.bak

You may get an error about the public schema, this is normal.

Get the database ready and set up Django:

$ docker-compose exec openrem python manage.py migrate --fake-initial

$ docker-compose exec openrem python manage.py makemigrations remapp

$ docker-compose exec openrem python manage.py migrate --fake

$ docker-compose exec openrem python manage.py collectstatic --noinput --clear

$ docker-compose exec openrem python django-admin compilemessages

The OpenREM server should now be ready to use again.

Advanced¶

These methods should not be required in normal use; only do this if you know what you are doing!

psql¶

Start the PostgreSQL console:

$ docker-compose exec db psql -U openremuser openrem_prod

-- List users
\du

-- List databases
\l

-- Exit the console
\q

pgAdmin or other PostgreSQL connections¶

To access the database directly by pgAdmin or other software, the ports must be exposed.

Edit docker-compose.yml to add the ports:

db:
  ports:
    - 5432:5432

If you have a database already running on the host machine, this port will prevent the container starting. In this case, change the first number in the pair to an alternative port.
The service will be accessible on the host machine after the containers are taken down and up again:

$ docker-compose down
$ docker-compose up -d

Linux installations¶

Database backup¶

Check the database username and change in the command below as necessary (openremuser)
Check the database name and change in the command below as necessary (openremdb)
You will need the password for openremuser
Ad hoc:

$ sudo -u postgres pg_dump -U openremuser -d openremdb -F c -f openremdump.bak

To automate a regular backup (recommended) adapt the following command in a bash script:

#! /bin/bash
rm -rf /path/to/db/backups/*
PGPASSWORD="mysecretpassword" /usr/bin/pg_dump -U openremuser -d openremdb -F c -f /path/to/db/backups/openremdump.bak

Database restore¶

Requires the same version of OpenREM to be installed as the database was exported from, unless you are Upgrading a native Linux install or Upgrading to a new Linux server.
Username can be changed on restore by specifying the new user in the restore command. The user must exist in PostgreSQL though - sudo -u postgres createuser -P openremuser if required
openrem/remapp/migrations/ should be empty except __init__.py

$ sudo -u postgres createdb -T template0 new_openremdb_name
$ sudo -u postgres pg_restore --no-privileges --no-owner -U openremuser -d new_openremdb_name path-to/openremdump.bak

Update the local_settings.py file with the new database details, as per Configure OpenREM
Set up the new database with Django/OpenREM after activating the virtualenv and moving to the site-packages/openrem folder:

$ python manage.py migrate --fake-initial
$ python manage.py migrate remapp --fake
$ python manage.py makemigrations remapp
$ python manage.py migrate

Windows installations¶

Database backup¶

Check the database username and change in the command below as necessary (openremuser)
Check the database name and change in the command below as necessary (openremdb)
You will need the password for openremuser
You will need to edit the command for the path to pg_dump.exe - the 14 is likely to be a lower number
Ad hoc:

C:\Users\openrem>"c:\Program Files\PostgreSQL\14\bin\pg_dump.exe" -U openremuser -d openremdb -F c -f windump.bak

To automate a regular backup (recommended) adapt the following command in a bat script:

Warning

Content to be added!

Database restore¶

Requires the same version of OpenREM to be installed as the database was exported from, unless you are Upgrading a native Windows install or Upgrading to a new Windows server.
Username can be changed on restore by specifying the new user in the restore command. The user must exist in PostgreSQL though - create the user in pgAdmin if required
openrem\remapp\migrations\ should be empty except __init__.py

C:\Users\openrem>"c:\Program Files\PostgreSQL\14\bin\pg_restore.exe" --no-privileges --no-owner -U openremuser -d openremdb -W windump.bak

Update the local_settings.py file with the new database details, as per Configure OpenREM
Set up the new database with Django/OpenREM after activating the virtualenv and moving to the site-packages\openrem folder:

(venv) E:\venv\Lib\site-packages\openrem>python manage.py migrate --fake-initial
(venv) E:\venv\Lib\site-packages\openrem>python manage.py migrate remapp --fake
(venv) E:\venv\Lib\site-packages\openrem>python manage.py makemigrations remapp
(venv) E:\venv\Lib\site-packages\openrem>python manage.py migrate

Advanced server configuration¶

Webserver configuration¶

Webserver timeout¶

Some long running actions can cause webserver errors if they take longer than the timeout setting in the webserver, particularly generating fluoroscopy Skin dose maps. The default setting is 300 seconds, or five minutes. To modify this, change the following two settings:

Edit docker-compose.yml in the Docker OpenREM installation folder and change the timeout setting on the following line:

services:
  openrem:
    container_name: openrem
    command: gunicorn openremproject.wsgi:application --bind 0.0.0.0:8000 --timeout 300

Edit nginx-conf/conf.d/openrem.conf and set the same timeout:

server {
    listen 80;
    location / {
        proxy_pass http://openremproject;
        # ...
        proxy_read_timeout 300s;
    }

Reload the containers:

$ docker-compose down
$ docker-compose up -d

Non-Docker install¶

Change the same settings as for the Docker install above:

$ sudo nano /etc/nginx/sites-available/openrem-server

and

$ sudo nano /etc/systemd/system/openrem-gunicorn.service

ExecStart=/var/dose/veopenrem3/bin/gunicorn \
    --bind unix:/tmp/openrem-server.socket \
    openremproject.wsgi:application --timeout 300

Adding an SSL certificate¶

It is advisable to add an SSL certificate to the web server even though it might only be accessible within an institution. There are several reasons for this, but one main one is that over time web browsers will give more and more warnings about entering passwords into non-HTTPS websites.

It is likely that within your institution there will be a corporate trusted root certificate and a mechanism of getting certificates you generate for your servers signed by that root certificate. How to generate a certificate signing request (CSR) and private key are beyond the scope of these documents, but this blog post was helpful when we were learning how to do this at our institution: https://www.endpoint.com/blog/2014/10/30/openssl-csr-with-alternative-names-one

Once you have a signed certificate, place it and the key in nginx-conf/certs, where it will be available in the Nginx container at /etc/ssl/private.

There are two conf files in nginx-conf/conf.d - the default one is openrem.conf. There is an alternative one named openrem-secure.conf.example. Edit the second file as required, then rename them both so the secure version is the only one to have a .conf ending.

Ensure the the following lines are updated for the name of your server and the names of your signed certificate and key:

server {
    listen 443 ssl;
    server_name add_server_name_here;
    ssl_certificate /etc/ssl/private/openrem.cer;
    ssl_certificate_key /etc/ssl/private/openrem.key;

    # ...
}

Running the OpenREM website in a virtual directory¶

If you want to run the OpenREM in a virtual directory (like http://server/dms/) you need to configure this in your web server configuration as well as in the OpenREM configuration.

The following steps are necessary:

Configure virtual directory settings in the Docker .env.prod file
Update Nginx webserver configuration
Update the reverse.js file
Restart the containers

Docker setup¶

Stop the containers if they are running before changing the configuration, using a shell (command prompt) in the Docker OpenREM installation folder

$ docker-compose down

Configure virtual directory settings in .env.prod¶

Django needs to know the virtual directory name and which URLs the static and media files are served from.

Edit .env.prod, uncomment the following lines (remove the #) and set them as appropriate. The VIRTUAL_DIRECTORY setting must have a trailing /. For example, to serve the website from a subfolder/virtual directory named dms:

## For installations in a virtual directory
VIRTUAL_DIRECTORY=dms/
MEDIA_URL=/dms/media/
STATIC_URL=/dms/static/

Modify webserver configuration¶

Edit nginx-conf/conf.d/openrem.conf to update the locations — again using the example virtual directory dms:

server {
    listen 80;
    location /dms/ {
        proxy_pass http://openremproject;
        # ...
    }
    location /dms/static/ {
        alias /home/app/openrem/staticfiles/;
    }
    location /dms/media/ {
        alias /home/app/openrem/mediafiles/;
    }
}

Start the containers¶

$ docker-compose up -d

Update reverse.js¶

The static reverse.js file should be updated in order to change the URLs in the static javascript files.

Open a shell (command prompt) and navigate to the Docker OpenREM installation folder

$ docker-compose exec openrem python manage.py collectstatic_js_reverse

Test!¶

You should now be able to reach the OpenREM interface using the virtual directory address.

Non-Docker install¶

$ sudo systemctl stop openrem-gunicorn.service
$ sudo systemctl stop nginx.service

Update local_settings.py¶

Update local_settings.py with the same variables as in the .env.prod file. If the values aren’t in your copy of the file just add them in:

$ cd /var/dose/veopenrem3/lib/python3.10/site-packages/openrem/
$ nano openremproject/local_settings.py

VIRTUAL_DIRECTORY = "dms/"
STATIC_URL = "/dms/static/"
MEDIA_URL = "/dms/media/"

Modify webserver configuration¶

$ sudo nano /etc/nginx/sites-available/openrem-server

server {
    # ...
    location /dms/static {
        alias /var/dose/static;
    }
    location /dms {
        proxy_pass http://unix:/tmp/openrem-server.socket;
        # ...
    }
}

Update reverse.js¶

$ . /var/dose/veopenrem3/bin/activate
$ cd /var/dose/veopenrem3/lib/python3.8/site-packages/openrem/
$ python manage.py collectstatic_js_reverse

Restart the services¶

$ sudo systemctl start openrem-gunicorn.service
$ sudo systemctl start nginx.service

Start all the services¶

Test web server¶

In a shell/command window, move into the openrem folder:

Ubuntu linux: /usr/local/lib/python2.7/dist-packages/openrem/
Other linux: /usr/lib/python2.7/site-packages/openrem/
Linux virtualenv: vitualenvfolder/lib/python2.7/site-packages/openrem/ (remember to activate the virtualenv)
Windows: C:\Python27\Lib\site-packages\openrem\
Windows virtualenv: virtualenvfolder\Lib\site-packages\openrem\ (remember to activate the virtualenv)

Web access on OpenREM server only¶

Run the built in web server:

python manage.py runserver --insecure

In a web browser on the same computer, go to http://localhost:8000/ - you should now see the message about creating users.

Web access on other computers¶

The built-in webserver only provides a service on the computer OpenREM is installed on by default (it’s only there really for testing). To view the OpenREM interface on another computer, you need to modify the runserver command:

python manage.py runserver --insecure 0.0.0.0:8000

This will enable the web service to be available from other computers on the network. If your server has several network cards and you want to restrict it to one, then you can use a real address rather than 0.0.0.0. Likewise you can specify the port (here it is 8000).

In a web browser on a different computer on the same network, go to http://192.168.1.10:8000/ (changing the IP address to the one you are running the server on) and you should see the OpenREM interface and the message about creating users.

Note

Why are we using the --insecure option? With DEBUG mode set to True the test web server would serve up the static files. In this release, DEBUG mode is set to False, which prevents the test web server serving those files. The --insecure option allows them to be served again.

Configure the settings¶

Follow the link presented on the front page to get to the user and group administration.

Initial home page with no users in groups

After the first users are configured, this link will no longer be presented and instead you can go to Config -> Users.
You will need the superuser username and password you created just after creating the database. The groups are
- viewgroup can browse the data only
- importsizegroup can use the csv import facility to add patient height and weight information
- importqrgroup can use the DICOM query-retrieve facility to pull in studies, as long as they are pre-configured
- exportgroup can view and export data to a spreadsheet
- pidgroup can search using patient names and IDs depending on settings, and export with patient names and IDs if they are also a member of the exportgroup
- admingroup can delete studies, configure DICOM Store/QR settings, configure DICOM keep or delete settings, configure patient ID settings, and abort and delete patient size import jobs. Members of the admingroup no longer inherit the other groups permissions.

In addition to adding users to these groups, you may like to grant a second user superuser and staff status so that there are at least two people who can manage the users
Return to the OpenREM interface (click on View site at the top right)

Link from Django user admin back to OpenREM

Follow the link to see more information about how you want OpenREM to identify non-patient exposures, such as QA. See Not-patient indicator settings.
Go to Config -> DICOM object delete settings and configure appropriately (see Delete objects configuration)
Go to Config -> Patient ID settings and configure appropriately (see Patient identifiable data)
If you want to use OpenREM as a DICOM store, or to use OpenREM to query remote systems, go to Config -> Dicom network configuration. For more information go to Importing data to OpenREM.
With data in the system, you will want to go to Config -> View and edit display names and customise the display names. An established system will have several entries for each device, from each time the software version, station name or other elements changes. See Display names and user-defined modalities for more information

Start using it - add some data!¶

See Importing data to OpenREM

Configuration and administration¶

Home page options ¶

Contents

Home page options
- Display of workload information

Display of workload information ¶

Config options — Figure 1: The `Config` menu (user and admin)¶

The home page can be configured to show the number of studies carried out in the past 7 (default) and 28 (default) days for each system. These default values can be changed by logging in, clicking on the Config menu at the right-hand end of the navigation bar, and then selecting the Home page options entry under User level config shown in the upper section of figure 1. This takes the user to a page where the two time periods can be viewed and updated (figure 2).

By default the display of workload information is disabled; this can be changed by an OpenREM administrator via the Home page options. When an OpenREM administrator views the home page options a tick box is included that enables or disables the display of workload data on the home page (figure 3).

Figure 3: The home page options admin form¶

When workload information is displayed, the link to the system data is modified in the workload cells to filter the studies to the same date range.

Delete objects configuration¶

OpenREM is able to automatically delete DICOM objects if they can’t be used by OpenREM or if they have been processed. This has the following advantages:

The server doesn’t need to have much storage space
It can help with information governance if the database is set to not store patient identifiable data (see Patient identifiable data)

Warning

If OpenREM is set to delete objects and you pass a local file to OpenREM using the command line, the source file will be deleted (as long as the filesystem permissions allow).

Configure what is deleted¶

Use the Config menu and select DICOM object deletion:

This will open the configuration page:

Modify DICOM object delete settings — Modify DICOM object deletion policy¶

The initial settings are to not delete anything. However, you are likely to want to delete objects that don’t match any import filters, and also to delete images such as mammo, DX and Philips CT, as these will take up space much more quickly than the radiation dose structured reports.

Reviewing the settings¶

When you have set your preferences, you will be redirected to the DICOM network configuration page, where at the bottom you can review the current settings:

DICOM object deletion poligy review on DICOM config page — Deletion policies can be reviewed on the DICOM network configuration page¶

More information about the DICOM network configuration can be found on the Direct from modalities page.

Display names and user-defined modalities ¶

Contents

Display names and user-defined modalities

The display name field ¶

Previous versions of OpenREM used each X-ray system’s DICOM station name as the identifier for each X-ray system. The front page showed a summary of the number of studies for each unique station name stored in the system. This led to a problem if multiple X-ray systems used the same station name: the OpenREM home page would only show one station name entry for these systems, with the number of studies corresponding to the total from all the rooms. The name shown alongside the total was that of the system that had most recently sent data to the system.

This issue has been resolved by introducing a new field called display name. This is unique to each piece of X-ray equipment, based on the combination of the following eight fields:

manufacturer

institution name

station name

department name

model name

device serial number

software version

gantry id

The default text for display name is set to a combination of institution name and station name. The default display name text can be changed by a user in the admingroup — see Setting display name automatically for known devices

User defined modality field ¶

OpenREM determines the modality type of a system based on the information in the DICOM radiation dose structured report. However sometimes this mechanism fails because vendors use templates meant for RF also for DX systems. Therefore it is possible from version 0.8.0 to set a modality type for each system manually. A manually set modality type overrides the automatically determined value.

Viewing X-ray system display names and user defined modality ¶

User options menu — The `Config` menu (user)¶

If you log in as a normal user then the Config menu becomes available at the right-hand end of the navigation bar at the top of the screen.

The third option, View display names & modality, takes you to a page where you can view the list of X-ray systems with data in OpenREM together with their current display name and user defined modality. If the user defined modality is not set, the value contains None. The X-ray systems are grouped into modalities and displayed in five tables: CT; mammography; DX and CR; fluoroscopy; and other.

List of current display names — Example list of display names¶

Setting display name automatically for known devices ¶

If you are a member of the admingroup you can set an option to automatically set the display name of already known devices even if one of the above mentioned fields changed. A device can send its Device Observer UID (especially in rdsr-objects). This is a unique ID for the device. If this UID is received by OpenREM it can set the display name and modality type the same as an already known device with the same Device Observer UID. This option can be useful if other parameters that OpenREM looks at frequently change. If you want to see if one of the other parameters changed (like software version), don’t tick this option.

Changing X-ray system display names and user defined modality ¶

If you wish to make changes to a display name or to the user defined modality then you must log in as a user that is in the admingroup. You will then be able to use the Display names & modality item under the Config menu:

This will take you to a page where you can view the list of X-ray systems with data in OpenREM. If you wish to change a display name or the user defined modality then click on the corresponding row. The resulting page will allow you to edit these parameters. Click on the Update button to confirm your changes:

Update a display name — Example of the page for updating a display name and user defined modality¶

You can change multiple rows at once. For display names you may wish to do this if a system has a software upgrade, for example, as this will generate a new default display name for studies carried out after the software upgrade has taken place. The studies from these will be grouped together as a single entry on the OpenREM homepage and individual modality pages.

If you update the user defined modality, the modality type for already imported studies will also be set to the user defined modality type. Only changes from modality DX (planar X-ray) to RF (fluoroscopy) and vice versa are possible.

Dual modality systems ¶

Some systems are dual purpose in that they can be used in both standard planar X-ray mode and in fluoroscopy mode. For these systems you can configure them as ‘Dual’ and OpenREM will attempt to reprocess all the studies related to the rows you have selected and assign them to DX or RF. The studies will then be displayed in the right sections in the web interface and will export correctly. New RDSRs relating to that X-ray system will be assigned a modality in the same way.

After an X-ray system has been set to Dual you may wish to reprocess the studies to assign modality again. To do this you can use the ‘reprocess’ link in the ‘User defined modality’ cell:

Reprocess Dual link — Re-sort studies into planar X-ray and fluoroscopy¶

Review of studies that failed to import ¶

Studies that have failed early in the import process might not have an entry in the unique_equipment_name table, and therefore will not appear in any of the other tables on this page. The table at the end allows the user to review these studies and delete them. See Failed import studies for more details.

Systems where Device Observer UID is not static ¶

OpenREM users have found one x-ray system which incorrectly sets the Device Observer UID to be equal to the Study Instance UID. In this situation a new entry is created in the display name settings for every new exam that arrives in OpenREM, making the display name table fill with many duplicate entries for the same system. To avoid this problem a list of models can be specified using the variable below - OpenREM will ignore the Device Observer UID value when creating new display names for any model in this list. The model name text must exactly match what is contained in the system’s Manufacturer’s Model Name DICOM tag (0008,1090).

IGNORE_DEVICE_OBSERVER_UID_FOR_THESE_MODELS = ['GE OEC Fluorostar']

For Docker installations, this setting is in the Docker env configuration.
For Linux installations, see the Configure OpenREM docs.
For Windows installations, see the Configure OpenREM docs.

Not-patient indicator settings¶

The standard configuration for OpenREM is to not store any patient identifiable information. Therefore it can be difficult to distinguish between real patients and test or quality assurance exposures.

Changed in 0.8.0

To aid identification of non-patient exposures, the patient name and the patient ID are checked against a set of patterns, and if a match is found then the pattern is recorded in the database before the patient name and ID are deleted or converted to a hash (see Patient identifiable data for details).

Setting the patterns to identify non-patient studies¶

Use the Config menu and select Not-patient indicators:

The patient name and the ID are matched against the patterns you configure. The patterns make use of wildcards as per the following table, and are case insensitive:

Pattern	Meaning
*	matches everything
?	matches any single character
[seq]	matches any character in seq
[!seq]	matches any character not in seq

To match all studies where the patient name begins with physics, the pattern should be set to physics*. This would match Physics^RoutIQ but not match Testing^Physics. The patient name in DICOM is normally formatted Family name^Given name^Middle name^Prefix^Suffix. Therefore to match any studies where the first name is Test, you would set the pattern to be *^test*.

If your test patient name always starts with PHY and then a number, you might use this pattern: phy[0-9]*. Here we have used a range for the sequence to match any number, but it will only match one character per sequence, so a * is required to match all the characters after the first number. This pattern will match Phy12345 and PHY6test but not Phyliss.

The pattern list for patient name and the list for patient ID are separate, so both need to be populated to meet your requirements.

Creating new patterns¶

Click on Add ID patterns or Add name patterns in the panel title bar and follow the instructions.

Modifying patterns¶

Click the Modify link in the row of the pattern you wish to modify.

Deleting patterns¶

Click the Delete link in the row of the pattern you wish to delete. You will be asked to confirm the deletion.

Replicating behaviour of release 0.7.4 and earlier¶

OpenREM releases before 0.8 had the not-patient identification patterns hard-coded. From release 0.8.0 the patterns are (admin) user configurable, but will start with no patterns in place. To add the patterns that would maintain the behaviour of previous releases, use the link at the bottom of the config page, or the link in the add/modify pages.

Patient identifiable data¶

Prior to version 0.7, no data that is generally considered to be patient identifiable was stored in the OpenREM database.

The following patient descriptors have always been recorded if they were available:

Patient age at the time of the study, but not date of birth (though this could be calculated from age)
Patient sex
Patient height
Patient weight

In addition, a key identifier for the exam that is normally not considered patient identifiable was stored:

Study accession number

It has become apparent that there are reasons where people need to store patient identifiable data to make the most of OpenREM, so this is now configurable from version 0.7 onwards.

Configure what is stored¶

On the Config menu, select Patient ID:

The initial settings are as follows:

Modify patient identifiable data settings

The default for patient name, ID and date of birth is to not store them. There isn’t an option currently to not store the accession number, though OpenREM continues to work if it is missing.

To store patient identifiable data from now on, select the relevant box and press Submit. If you change the setting again later, then data already stored will remain in the database.

Store encrypted data only¶

If you wish to have the patient name and/or ID available for finding studies relating to a specific patient, but do not need to identify who that patient is, then it is possible to create an ‘encrypted’ version of the ID or name. In this case, a one-way SHA 256 hash is generated and the hash value is stored instead.

If exactly the same name or ID (including spelling, spacing, case etc) occurs more than once, then the same hash will be generated.

The same applies to accession numbers if the option to encrypt the accession number is selected.

Using patient identifiable data¶

Querying for patient studies¶

In the modality pages of the OpenREM web interface, if you are in the pidgroup you will have a filter for patient name and patient ID available:

If the values in the database are not encrypted, then partial search terms can be used as a case-insensitive ‘contains’ query will be applied.

If the values are encrypted, then only the entire string, with exactly the same case, spacing and punctuation will match. This is more likely to be successful with patient ID than with patient name.

Study export with patient identifiers¶

Users in the pidgroup will have extra export buttons available in the modality pages:

If the IDs or names are encrypted, then these columns will contain the hash rather than the original values. However, it will be possible to see if more than one study belongs to one patient as the values should be the same for both. Due to the nature of the algorithm however, a single change in the name or ID - such as an upper case letter instead of a lower case one - will be recorded as a completely different hash value.

Any exports with either patient name or patient ID included will also have a date of birth column.

Deleting studies¶

Individual studies¶

If you log in as a user that is in the admingroup, then an extra column is appended in the filtered view tables to allow studies to be deleted:

Clicking on delete takes you to a confirmation page before the delete takes place.

All studies from one source¶

If you log in as a user that is in the admingroup, on the Config menu select Display names & modality to get to a list of all the X-ray systems with data in OpenREM. More information about Display names and user-defined modalities.

Each row is a unique combination of all the column headers, so if a modality has a software update for example this will usually mean a new row is started.

In the last column is a link to Review the studies from that source. This can be useful for troubleshooting a particular source, or you can use it to delete all the studies from one source in one go.

Source study review — Source equipment review page with study delete options¶

The details for that source are displayed, along with a table showing which sort of data is contained in each study. Above the ‘Study deletion options’ panel the following two numbers are indicated:

The number of studies associated with this equipment
The number of studies associated with this equipment after being filtered by the indicated modality type

If the second number is smaller than the first, this will indicate that some of the studies from the equipment have been labelled with a different modality type. There will therefore be an entry in one of the other tables on the equipment display name page.

Delete studies and table entry¶

Use this button if you want to delete all the studies and remove the entry that has been made in the Unique Equipment Names table. Otherwise, the entry would remain but with zero studies associated with it. The deletion takes a second confirmation step.

If there are studies associated with this equipment that are listed with a modality type different to the one shown, those studies will not be deleted and the table entry will not be removed.

Delete studies¶

If you have associated this table entry with a Display name and you want any future studies to fall under the same name, you can leave the entry in the Unique Equipment Names table. You might want to do this for example if you have a Display name of ‘CR’ or ‘Imported’. Again, there is a confirmation step.

Again, only the studies associated with this equipment that have the same modality type as shown will be deleted.

Failed import studies¶

At the bottom of the Display names & modality page is a table listing the number of studies that are in the database, but do not have an entry in the unique_equipment_name table. This usually indicates a study that has failed early in the import process.

Users in the admingroup are able to click on the links to review the studies on a per-modality basis. This will list the information that is available, which might indicate which system they came from, what times, dates and accession numbers.

The user is then able to delete all the failed import studies in the list.

Before release 0.8.2, these studies would appear in the homepage listing as Error has occurred - import probably unsuccessful. This has now changed to a link to the review page for that modality with the text Failed import - review here for users in the admingroup and Failed import - ask an administrator to review for other users.

Adding patient size information from csv using the web interface ¶

Contents

Adding patient size information from csv using the web interface
Adding patient size information from csv using the command line

Uploading patient size data ¶

If you log in as a user that is in the admingroup, then a menu is available at the right hand end of the navigation bar:

The first option takes you to a page where you can upload a csv file containing details of the patient height and weight, plus either the accession number or the Study Instance UID.

Uploading CSV files containing patient size information

The csv file needs to have at least the required columns. Additional columns will be ignored. If your source of patient size data does not have either the height or the weight column, simply add a new empty column with just the title in the first row.

When you have selected the csv file, press the button to upload it.

Importing the size data to the database ¶

On the next page select the column header that corresponds to each of the head, weight and ID fields. Also select whether the ID field is an Accession number or a Study UID:

When the column headers are selected, click the ‘Process the data’ button.

The progress of the import is then reported on the patient size imports page:

During the import, it is possible to abort the process by clicking the button seen in the image above. The log file is available from the completed table whether it completed or not - there is no indication that the import was aborted.

As soon as the import is complete, the source csv file is deleted from the server.

Reviewing previous imports ¶

After an import is complete, it is listed in the completed import tasks table. You can also get to this page from the Admin menu:

For each import, there is a link to the logfile, which looks something like this. With this import accession numbers weren’t available so the patient size information was matched to the study instance UID:

Deleting import logs ¶

The completed import tasks table also has a delete check box against each record and a delete button at the bottom. The csv file originally imported has already been deleted - this delete function is to remove the record of the import and the log file associated with it from the database/disk.

Adding patient size information from csv using the command line ¶

Usage:

openrem_ptsizecsv.py [-h] [-u] [-v] csvfile id height weight

-h, --help: Print the help text.
-u, --si-uid: Use Study Instance UID instead of Accession Number.
-v, --verbose: Print to the standard output the success or otherwise of inserting each value.
csvfile: csv file containing the height and/or weight information and study identifier. Other columns will be ignored. Use quotes if the filepath has spaces.
id: Column title for the accession number or study instance UID. Use quotes if the title has spaces.
height: Column title for the patient height (DICOM size) - if this information is missing simply add a blank column with a suitable title. Use quotes if the title has spaces.
weight: Column title for the patient weight - if this information is missing simply add a blank column with a suitable title. Use quotes if the title has spaces.

Fluroscopy high dose alerts ¶

Contents

Fluroscopy high dose alerts

Alert level configuration ¶

The system highlights fluoroscopy studies that have exceeded defined levels of DAP, total dose at reference point and peak skin dose. These alert levels can be configured by an OpenREM administrator via the Fluoro alert levels option in the Config menu (figure 1).

The default alert levels are 20000 cGy.cm² DAP, 2 Gy total dose at reference point and 2 Gy peak skin dose(figure 2).

Figure 2: Fluoroscopy high dose alert settings¶

Figures 3 and 4 illustrate how studies that exceed an alert level are highlighted in the filtered and detailed fluoroscopy views.

Figure 3: Filtered view showing the highlighting of some high dose studies¶

Figure 4: Detailed view showing high-dose highlighting¶

Alerts for cumulative dose over a period of time ¶

As well as alerting to individual studies that exceed alert levels the system can be configured to calculate cumulative dose over a defined number of weeks for studies with matching patient IDs. When this is activated, for each study OpenREM looks for earlier fluoroscopy studies that have taken place that share the same patient ID, or encrypted patient ID, and sums the study DAP and total dose at reference point values. The time period that is used is configured by an OpenREM administrator, and defaults to 12 weeks (figure 2). This feature has not yet been implemented for the skin dose.

For this feature to work the storage of patient ID or encrypted patient ID must be enabled (see the Patient identifiable data documentation).

The configuration settings for this feature are (figure 2):

The number of previous weeks over which to sum DAP and dose at RP for studies with matching patient ID is defined in the options
The display of summed DAP and dose at RP values in the fluoroscopy filtered and detailed views, and in e-mail notifications
The automatic calculation of summed DAP and dose at RP for new studies imported into OpenREM

An example of a study where there is another study with matching patient ID is shown below in figure 5. In this example neither of the two individual studies had doses that exceeded an alert level, but when summed together the total dose at RP does exceed the corresponding alert.

Figure 5: Detailed view showing associated studies over a time period¶

Recalculation of summed data ¶

After upgrading from a version of OpenREM prior to 0.8.2, or after changing the alert levels or number of weeks to look for matching data, the summed dose values must be recalculated. The user is prompted to do this via the display of an orange button, as shown in figure 6 below. If settings have changed an information message is also displayed at the top of the screen.

Figure 6: Prompt to recalculate the summed dose values¶

Recalculation of the summed data is likely to take several minutes. During this time the form buttons are faded out and disabled, and a spinning icon is shown in the middle of the page (figure 7). The user must remain on this page until the calculations are complete.

Recalculating the summed dose values — Figure 7: Prompt to recalculate the summed dose values¶

Once all summed data has been recalculated the orange recalculate button is hidden, the other form buttons are reactivated and the user is shown a success message at the top of the screen (figure 8, below).

E-mail notifications of high dose alerts ¶

For this feature to function the e-mail section in local_settings.py must be correctly completed (see the : ref :email_configuration documentation) and the e-mail server must allow sending of messages that originate from the OpenREM server, or from the authenticated user specified in the e-mail settings.

OpenREM users can be automatically sent e-mail notifications of studies that have exceeded a high dose alert level. This feature can be enabled or disabled by an OpenREM administrator on the High dose alerts configuration page as shown in figure 2 above.

Alert recipients users are chosen by navigating to the Fluoro alert notifcation page via the Config menu. Figure 9 shows an example of the notification page.

It should be noted that any OpenREM user selected to receive high dose alerts must have an e-mail address entered in their user profile.

Figure 9: E-mail user-notification of high-dose alerts¶

Task management ¶

Contents

Task management

Viewing task and service statuses ¶

Users who are logged in with admin rights can use the Tasks menu and choose All tasks to see the following:

A list of the tasks currently being executed
A list of previous tasks and their final status. If any errors occurred they will be displayed here.

Task and service status — Figure 2: The task administration page¶

Terminating running tasks ¶

It is possible to terminate any active tasks by clicking the red button. There is no confirmation step. Note that this immediately interrupts everything this process was doing so far, leading to things like partially imported studies. In general this should not be an issue (in case of aborted imports they should be completed when you start importing them again), but note that there is a certain risk in killing tasks and use this only as a last resort.

A note on move: executing a move will create a task which then produces import tasks for all the studies it should import. This means if you intend to abort a move you should abort the task with Task type “move” and not the import tasks started by that process!

Configuring the size of task history ¶

The status of 2000 active, recent and older tasks are stored in the OpenREM database. This limit can be altered by users who are logged in with admin rights by clicking on Task settings in the Config menu and changing the current value. If this limit is set to a very high value it can cause the web browser to run out of memory when trying to view the Task page due to the large number of rows in the tables.

Importing data to OpenREM¶

From local DICOM files¶

If you have RDSRs or RRDSRs, DX, MG, PET or NM images or Philips CT Dose Info images, you can import them directly into OpenREM:

Importing from DICOM files¶

If you are using linux, or for Windows if you have put C:\Python27\;C:\Python27\Lib\site-packages;C:\Python27\Scripts onto your system path, you should be able to import from the command line:

Radiation Dose Structured Reports¶

openrem_rdsr.py filename.dcm

You can use wildcards to process a number of files at once, ie:

openrem_rdsr.py *.dcm

Cumulative and continued study RDSRs¶

Background¶

Cumulative RDSRs¶

Some modalities are configured to send an RDSR after every exposure, with each new RDSR containing a complete record of the examination up to that point. For example, this is what the current version of the Siemens CT scanner software does.

Continued study RDSRs¶

On most systems the RDSR is sent when the study is completed. If the study is then restarted, the system must create a new RDSR. On a Siemens CT system, this new RDSR will have the same Study Instance UID and the same accession number, but the content will only refer to the continued study, not the original study.

Pre-0.8.0 OpenREM behaviour¶

Prior to release 0.8.0, OpenREM would check the Study Instance UID on import and check the value against the existing studies in the database. If a match was found, then the new RDSR was rejected on the basis that it must be a duplicate.

This would therefore ignore both cumulative and continued study RDSRs which means your database might be filled with single event studies, and you won’t have details of any continued studies.

Current OpenREM behaviour¶

New imports¶

On import of the first RDSR in a study, the SOP Instance UID of the RDSR is recorded with the study. This is an ID that is unique to that RDSR object - any further RDSRs might have the same Study Instance UID, but will always have a different SOP Instance UID.

When the second RDSR is imported, the duplicate StudyInstanceUID will trigger OpenREM to check the SOP Instance UID of the new RDSR against the one(s) stored with that study. If there is a match, the new RDSR is ignored as it has already been processed. If it does not match, then the Irradiation Event UID of each exposure in the new RDSR is compared to the Irradiation Event UIDs already in the database for that study, to establish if the new RDSR carries new information that should be imported.

In the case of a cumulative RDSR that is sent after each event, the original study is deleted from the database and is replaced by the newer one if it has additional events.

In the case of a continued study RDSR which has a completely different set of events, the new RDSR is imported alongside the existing one.

Existing studies imported before 0.8.0¶

RDSRs imported before upgrading to 0.8.0 will not have the SOP Instance UID recorded in the database and so the new RDSR will be compared at event level with the existing study before making an import decision, as with new studies.

Fixing existing studies¶

Importing from file¶

If you are have a store of the RDSRs that were previously rejected, import them all again and this time they should be processed properly.

For example on my system, using linux, each scanner started sending per-exposure RDSRs from the date they were upgraded. I found the RDSRs from that date to the date I upgraded OpenREM and imported them:

touch --date "2018-01-06" tmpdate20180106
touch --date "2018-02-07" tmpdate20180207
find RDSRs/ -newer tmpdate20180106 ! -newer tmpdate20180207 -name *.dcm -exec openrem_rdsr.py {} \;

Importing via query-retrieve¶

The query-retrieve duplicates processing has been updated to compare SOP Instance UIDs returned by the remote node (the PACS) with the SOP Instance UIDs stored with each study in OpenREM. Therefore, after an initial import of each RDSR in your search, any subsequent query should drop any RDSRs that have previously been processed and not move them a second time.

Radiopharmaceutical Radiation Dose Structured Reports¶

You can use the same import script as for Radiation Dose Structured Reports.

For mammography DICOM images¶

openrem_mg.py filename.dcm

The facility for extracting dose information from mammography DICOM images has been designed and tested with images created with the GE Senographe DS. It has now also been used with the images generated by the following systems:

GE Senographe Essential

Hologic Selenia

Siemens Inspiration

For radiographic DICOM images¶

openrem_dx.py filename.dcm

For PET/NM DICOM images¶

openrem_nm.py filename.dcm

Note that more complete information can be loaded from the RRDSRs if available. For PET images the PET series information can be added to the RRDSR data.

For CT dose summary files from Philips CT scanners¶

openrem_ctphilips.py filename.dcm

This extractor makes use of the information stored in the header data of the Philips Secondary Capture object with a series description of ‘Dose Info’. The value inserted into ‘Study description’ in the OpenREM database is actually taken from the Protocol field. The value in Study description is inserted into the study level comment field in the database, along with the protocol file name and any ‘comments on radiation dose’.

For CT dose summary files from older Toshiba CT scanners¶

openrem_cttoshiba.py path_to_files

This extractor is designed to create a DICOM radiation dose structured report from the information contained in secondary capture dose summary images, supplemented by data stored in image tags. It requires a folder of DICOM objects as input (suitable data can be retrieved from a DICOM node using the qrscu.py command with the -toshiba switch - see Query-retrieve using the command line interface). It creates an initial RDSR from the secondary capture dose summary, and then tries to enrich this with additional information contained in image tags. The routine attempts to extract the following information from the image tags and insert it into the initial RDSR:

Study-level information¶

Study description

Requested procedure description

Software versions

Device serial number

Series-level information¶

Protocol name

Exposure time (per rotation)

kVp

Spiral pitch factor

Nominal total collimation width

Nominal single collimation width

Exposure modulation type

The routine was developed for older Toshiba CT scanners that cannot create RDSR objects themselves. It is known to work with:

Toshiba CX, software version V4.40ER011

Toshiba CXL, software version V4.51ER014

Toshiba CXL, software version V4.86ER008 (this software version can produce RDSR objects directly, but may not populate some fields, such as requested procedure name and study description)

This extractor has also been used successfully on images from a GE LightSpeed Plus scanner, although in this case no supplementary data is extracted from image tags.

If you want some examples, you can find the DICOM files that we use for the automated testing in the openrem/remapp/tests/test_files folder in your OpenREM installation.

Direct from modalities¶

For production use, you will either need the modalities to send the RDSR or images directly to your OpenREM server using DICOM, or you will need to use query-retrieve to fetch the DICOM objects from the PACS or the modalities. In either of these situations, you will need to run a DICOM Store service on your OpenREM server.

DICOM Network Configuration¶

Configuring DICOM store nodes in OpenREM¶

You need to configure details of the DICOM store node to enable the query-retrieve functionality. You will also need to have installed Orthanc or an alternative:

Orthanc enabled by default in Docker
Installed in the Linux instructions and configured at DICOM Store SCP
Installed in the Windows instructions and configured at to be written

To configure a DICOM Store SCP, on the Config menu select DICOM networking, then click Add new Store and fill in the details (see figure 1):

Figure 1: DICOM Store SCP configuration¶

Name of local store node: This is the friendly name, such as OpenREM store
Application Entity Title of the node: This is the DICOM name for the store, and must be letters or numbers only, no spaces, and a maximum of 16 characters
Port for store node: Port 104 is the reserved DICOM port, but it is common to use high ports such as 8104, partly because ports up to 1024 usually need more privileges than for the high ports. However, if there is a firewall between the remote nodes (modalities, PACS) and the OpenREM server, then you need to make sure that the firewall is configured to allow the port you choose here

Status of DICOM Store SCP nodes¶

DICOM Store SCP advanced configuration

DICOM Store SCP status "Association fail" — Figure 3: DICOM Store SCP status - Alive and Association failed¶

DICOM Store SCP nodes that have been configured are listed in the left column of the DICOM network configuration page. For each server, the basic details are displayed, including the Database ID which is required for command line/scripted use of the query-retrieve function.

In the title row of the Store SCP config panel, the status will be reported either as ‘Server is alive’ or ‘Error: Association fail - server not running?’ - see figure 3

Query retrieve of third-party system, such as a PACS or modality¶

To Query-Retrieve a remote host, you will need to configure both a local Store SCP and the remote host.

To configure a remote query retrieve SCP, on the Config menu select DICOM networking, then click Add new QR Node and fill in the details:

Name of QR node: This is the friendly name, such as PACS QR
AE Title of the remote node: This is the DICOM name of the remote node, 16 or fewer letters and numbers, no spaces
AE Title this server: This is the DICOM name that the query (DICOM C-Find) will come from. This may be important if the remote node filters access based on calling aet. Normal rules of 16 or fewer letters and numbers, no spaces
Remote port: Enter the port the remote node is using (eg 104)
Remote IP address: The IP address of the remote node, for example 192.168.1.100
Remote hostname: Alternatively, if your network has a DNS server that can resolve the hostnames, you can enter the hostname instead. If the hostname is entered, it will be used in preference to the IP address, so only enter it if you know it will be resolved.

Now go to the DICOM Query Retrieve Service documentation to learn how to use it.

Troubleshooting: openrem_store.log¶

If the default logging settings haven’t been changed then there will be a log files to refer to. The default location is within your MEDIAROOT folder:

This file contains information about each echo and association that is made against the store node, and any objects that are sent to it.

The following is an example of the log for a Philips dose info image being received:

[21/Feb/2016 21:13:43] INFO [remapp.netdicom.storescp:310] Starting AE... AET:MYSTOREAE01, port:8104
[21/Feb/2016 21:13:43] INFO [remapp.netdicom.storescp:314] Started AE... AET:MYSTOREAE01, port:8104
[21/Feb/2016 21:13:43] INFO [remapp.netdicom.storescp:46] Store SCP: association requested
[21/Feb/2016 21:13:44] INFO [remapp.netdicom.storescp:54] Store SCP: Echo received
[21/Feb/2016 21:13:46] INFO [remapp.netdicom.storescp:46] Store SCP: association requested
[21/Feb/2016 21:13:46] INFO [remapp.netdicom.storescp:54] Store SCP: Echo received
[21/Feb/2016 21:13:49] INFO [remapp.netdicom.storescp:46] Store SCP: association requested
[21/Feb/2016 21:13:49] INFO [remapp.netdicom.storescp:54] Store SCP: Echo received
[21/Feb/2016 21:13:50] INFO [remapp.netdicom.storescp:46] Store SCP: association requested
[21/Feb/2016 21:13:50] INFO [remapp.netdicom.storescp:54] Store SCP: Echo received
[21/Feb/2016 21:13:51] INFO [remapp.netdicom.storescp:46] Store SCP: association requested
[21/Feb/2016 21:13:51] INFO [remapp.netdicom.storescp:54] Store SCP: Echo received
[21/Feb/2016 21:14:39] INFO [remapp.netdicom.storescp:46] Store SCP: association requested
[21/Feb/2016 21:14:39] INFO [remapp.netdicom.storescp:78] Received C-Store. Stn name NM-54316, Modality CT,
SOPClassUID Secondary Capture Image Storage, Study UID 1.2.840.113564.9.1.2843752344.47.2.5000947881 and Instance
UID 1.2.840.113704.7.1.1.4188.1234134540.349
[21/Feb/2016 21:14:39] INFO [remapp.netdicom.storescp:232] File
/var/openrem/media/dicom_in/1.2.840.113704.7.1.1.4188.1453134540.349.dcm written
[21/Feb/2016 21:14:39] INFO [remapp.netdicom.storescp:263] Processing as Philips Dose Info series
...etc

DICOM Store¶

The Orthanc DICOM server is recommended; another store can be used instead but documentation is not provided. Docker installs have the Orthanc server build-in. For non-Docker installs, instructions are included in the main installation documentation:

Linux: DICOM Store SCP
Windows: to be written

Query-retrieve from a PACS or similar¶

Before you can query-retrieve objects from a remote PACS, you need to do the following:

Create a DICOM Store service to receive the DICOM objects - see Direct from modalities above.
Configure OpenREM with the settings for the remote query-retrieve server:
Configuration required for query-retrieve¶

You need a DICOM store service set up - see Importing data to OpenREM for details.

If you are using a third party DICOM Store server, then you will need to add the details as per DICOM Network Configuration but do not use the ‘advanced’ section.

To configure a remote query retrieve SCP, on the Config menu select DICOM networking, then click Add new QR Node and fill in the details:
- Name of QR node: This is the friendly name, such as PACS QR
- AE Title of the remote node: This is the DICOM name of the remote node, 16 or fewer letters and numbers, no spaces
- AE Title this server: This is the DICOM name that the query (DICOM C-Find) will come from. This may be important if the remote node filters access based on calling aet. Normal rules of 16 or fewer letters and numbers, no spaces
- Remote port: Enter the port the remote node is using (eg 104)
- Remote IP address: The IP address of the remote node, for example 192.168.1.100
- Remote hostname: Alternatively, if your network has a DNS server that can resolve the hostnames, you can enter the hostname instead. If the hostname is entered, it will be used in preference to the IP address, so only enter it if you know it will be resolved.
- Use Modality in Study Query: Some PACS systems (like Impax 6.6) need modality at study level for correct filtering. if this option is checked, the modality tag is inserted in the study level request.
  
  Warning
  
  Modality is not a valid tag in a study level request (Modalities In Study is available instead). However, some PACS systems require it for proper function, others will ignore it, and some will return zero results if the tag is present.
Configure the settings of your DICOM store service on the PACS
Learn how to use it:
DICOM Query Retrieve Service¶

To query retrieve dose related objects from a remote server, you need to review the DICOM Network Configuration documents first to make sure you have created a DICOM Store node installed and configured which will import objects to OpenREM.

You will also need to set up the remote server to allow you to query-retrieve using it - the remote server will need to be configured with details of the store node that you have configured.
Query-retrieve using the web interface¶

Figure 1: Import Query-Retrieve menu¶
- On the Imports menu, select Query remote server - see figure 1. If the menu isn’t there, you need to check your user permissions – see Configure the settings for details.
- Each configured query-retrieve node and each local store node is automatically tested to make sure they respond to a DICOM echo - the results are presented at the top of the page. See figure 2 for an example.
Figure 2: Local and remote QR statuses¶
- Select the desired remote host, ie the PACS or modality you wish to query.
- Select the local store node you want to retrieve to.
- Select which modalities you want to query for - at least one must be ticked.
- Select a date range - the wider this is, the more stress the query will place on the remote server, and the higher the likelyhood of the query being returned with zero results (a common configuration on the remote host to prevent large database queries affecting other services). Defaults to ‘from yesterday’.
- If you wish to exclude studies based on their study description, enter the text here. Add several terms by separating them with a comma. One example would be to exclude any studies with imported in the study description, if your institution modifies this field on import. The matching is case-insensitive.
- Alternatively, you might want to only keep studies with particular terms in the study description. If so, enter them in the next box, comma separated.
- You can also exclude studies by station name, or only keep them if they match the station name. This is only effective if the remote system (the PACS) supports sending back station name information. By default, this is only checked against series level responses (changed in OpenREM 1.0).
Advanced query options¶

Attempt to get Toshiba dose images default not ticked: If you have done the extra installation and configuration required for creating RDSRs from older Toshiba scanners, then you can tick this box for CT searches to get the images needed for this process. See the logic description below for details.

Ignore studies already in the database default ticked: By default OpenREM will attempt to avoid downloading any DICOM objects (RDSRs or images) that have already been imported into the database. Untick this box to override that behaviour and download all suitable objects. See the logic description below for details.

Include SR only studies default not ticked: If you have a DICOM store with only the radiation dose structured reports (RDSR) in, or a mix of whole studies and RDSRs without the corresponding study, then tick this box. Any studies with images and RDSRS will be ignored (they can be found without this option). If this box is ticked any modality choices will be ignored.

Get SR series that return nothing at image level query default not ticked: If you have a DICOM store with SR series that you know contain RDSR objects, but when queried your store says they are empty, then check this box. If this behaviour is found, a message will be added to the openrem_qr.log at INFO level with the phrase Try '-emptysr' option?. With the box checked the query will assume any SR series found contains an RDSR. Warning: with this behavior, any non-RDSR structured report series (such as a radiologists report encoded as a structured report) will be retrieved instead of images that could actually be used (for example with mammography and digital radiographs). Therefore this option should be used with caution!

Check station name include/exclude at study level default not ticked: Change this setting to enable checking of station name include/exclude at study level instead of series level. This addresses issue #772 as some studies will have different station name information at study level than at series level - if both levels are checked it is impossible to get the desired response.

When you have finished the query parameters, click Submit
Review and retrieve¶

Once all the responses have been purged of unwanted modalities, study descriptions or study UIDs, the number of studies of each type will be displayed and a button appears. Click Retrieve to request the remote server send the selected objects to your selected Store node. This will be based on your original selection - changing the node on the left hand side at this stage will have no effect.

The progress of the retrieve is displayed in the same place until the retrieve is complete. You can also see the query and start the Retrieve in the DICOM query summary.
Query-retrieve using the command line interface¶
Running the command in different environments

Docker: In a command window/shell, navigate to the folder containing docker-compose.yml etc. Then precede the command with docker-compose exec openrem:

$ docker-compose exec openrem openrem_qr.py -h

Linux: Activate the virtualenv - assuming default Ubuntu install:

$ . /var/dose/veopenrem3/bin/activate $ openrem_qr.py -h

Windows: Activate the virtualenv - docs to be written - and command might need the full path?:

> C:\OpenREM\veopenrem3\Scripts\activate.bat > C:\OpenREM\veopenrem3\Scripts\openrem_qr.py -h
usage: openrem_qr.py [-h] [-ct] [-mg] [-fl] [-dx] [-nm] [-f yyyy-mm-dd] [-t yyyy-mm-dd] [-sd yyyy-mm-dd] [-tf hhmm] [-tt hhmm] [-e string] [-i string] [-sne string] [-sni string] [--stationname_study_level] [-toshiba] [-sr] [-dup] [-emptysr] qr_id store_id Query remote server and retrieve to OpenREM positional arguments: qr_id Database ID of the remote QR node store_id Database ID of the local store node options: -h, --help show this help message and exit -ct Query for CT studies. Cannot be used with -sr -mg Query for mammography studies. Cannot be used with -sr -fl Query for fluoroscopy studies. Cannot be used with -sr -dx Query for planar X-ray studies (includes panoramic X-ray studies). Cannot be used with -sr -nm Query for nuclear medicine studies. Cannot be used with -sr -f yyyy-mm-dd, --dfrom yyyy-mm-dd Date from, format yyyy-mm-dd. Cannot be used with --single_date -t yyyy-mm-dd, --duntil yyyy-mm-dd Date until, format yyyy-mm-dd. Cannot be used with --single_date -sd yyyy-mm-dd, --single_date yyyy-mm-dd Date, format yyy-mm-dd. Cannot be used with --dfrom or --duntil -tf hhmm, --tfrom hhmm Time from, format hhmm. Requires --single_date. -tt hhmm, --tuntil hhmm Time until, format hhmm. Requires --single_date. -e string, --desc_exclude string Terms to exclude in study description, comma separated, quote whole string -i string, --desc_include string Terms that must be included in study description, comma separated, quote whole string -sne string, --stationname_exclude string Terms to exclude in station name, comma separated, quote whole string -sni string, --stationname_include string Terms to include in station name, comma separated, quote whole string --stationname_study_level Advanced: Filter station name at Study level, instead of at Series level -toshiba Advanced: Attempt to retrieve CT dose summary objects and one image from each series -sr Advanced: Use if store has RDSRs only, no images. Cannot be used with -ct, -mg, -fl, -dx -dup Advanced: Retrieve duplicates (objects that have been processed before) -emptysr Advanced: Get SR series that return nothing at image level query
As an example, if you wanted to query the PACS for DX images on the 5th and 6th April 2010 with any study descriptions containing imported excluded, first you need to know the database IDs of the remote node and the local node you want the images sent to. To find these, go to the DICOM Network Configuration page where the database ID is listed among the other details for each node.

Assuming the PACS database ID is 2, and the store node ID is 1, the command would look something like:
$ docker-compose exec openrem openrem_qr.py 2 1 -dx -f 2010-04-05 -t 2010-04-06 -e "imported"
If you want to do this regularly to catch new studies, you might like to use a script something like this on Linux - make sure you comment out or delete one of the options, and amend as necessary!
#!/bin/bash ONEHOURAGO=$(date -d "1 hour ago" "+%Y-%m-%d") # Docker on Linux /usr/local/bin/docker-compose -f /path/to/docker-compose.yml exec -T openrem openrem_qr.py 2 1 -dx -f $ONEHOURAGO -t $ONEHOURAGO -e "Imported" # Linux /var/dose/veopenrem3/bin/python /var/dose/veopenrem3/bin/openrem_qr.py 2 1 -dx -f $ONEHOURAGO -t $ONEHOURAGO -e "Imported"
This script could be run once an hour using a cron job. By asking for the date an hour ago, you shouldn’t miss exams taking place in the last hour of the day. As the script won’t run from the folder containing docker-compose.yml the location of that file needs to be passed to docker-compose with the -f option. You can check the path to docker-compose on your system using which docker-compose.

A similar script could be created as a batch file or PowerShell script on Windows and run using the scheduler. An example PowerShell script is shown below:
# Script to obtain all CT studies from a DICOM node on the day prior to the # date the script is run and import them into OpenREM. # Get yesterday's date $dateString = "{0:yyyy-MM-dd}" -f (get-date).AddDays(-1) # Run the openrem_qr.py script with yesterday's date as the to and from date # Docker on Windows docker-compose -f C:\Path\To\docker-compose.yml exec -T openrem openrem_qr.py 2 1 -ct -f $dateString -t $dateString # Windows C:\OpenREM\veopenrem3\Scripts\python.exe C:\OpenREM\veopenrem3\Scripts\openrem_qr.py 2 1 -dx -f $ONEHOURAGO -t $ONEHOURAGO -e "Imported"
The above PowerShell script could be run on a regular basis by adding a task to the Windows Task Scheduler that executes the powershell program with an argument of -file C:\path\to\script.ps1.
Querying with time range¶

It is now possible to query for studies in a time window when using query-retrieve from the command line (web interface version will be introduced later). This can be particularly useful where PACS query responses are limited or null if the query matches too many studies.

Using the --tfrom/-tf and/or the --tuntil/-tt arguments are only allowed if --single_date/-sd argument is used.

Note: -sd 2018-03-19 is the same as using -f 2018-03-19 -t 2018-03-19, and can be used without the time arguments.

-tf used without -tt will search from tf until 23.59 that day.

-tt used without -tf will search from 00.00 to tt that day.

-tf and -tt used together will search from tf to tt.

For example, to search for CT from 12 noon to 3pm on 19th March 2018, using remote QR node database ID 2 and local store database ID 1:

$ # Using Docker on Linux $ docker-compose exec openrem openrem_qr.py 2 1 -ct -sd 2018-03-19 -tf 1200 -tt 1500
Query filtering logic¶
Study level query response processing¶

First we query for each modality chosen in turn to get matching responses at study level.

If the optional ModalitiesInStudy has been populated in the response, and if you have ticked Include SR only studies, then any studies with anything other than just SR studies is removed from the response list.

If any study description filters have been added, and if the StudyDescription tags are returned by the remote server, the study response list is filtered accordingly. The same applies to the station name filter if the option to check station names at study level has been selected.

For the remaining study level responses, each series is queried.

If ModalitiesInStudy was not returned, it is now built from the series level responses.

If the remote server returned everything rather than just the modalities we asked for, the study level responses are now filtered against the modalities selected.
Series level query processing¶

If station name filters have been added, and if the StationName tags are returned by the remote server, the series list is filtered accordingly — unless the option to check station names at study level has been selected.

If mammography exams were requested, and a study has MG in:

If one of the series is of type SR, an image level query is done to see if it is an RDSR. If it is, all the other series responses are deleted (i.e. when the move request/’retrieve’ is sent only the RDSR is requested not the images.

Otherwise the SR series responses are deleted and all the image series are requested.

If planar radiographic exams were requested, and a study has DX or CR in:

Any SR series are checked at ‘image’ level to see if they are RDSRs. If they are, the other series level responses for that study are deleted.

Otherwise the SR series responses are deleted and all the image series are requested.

If fluoroscopy exams were requested, and a study has RF or XA in:

Any SR series are checked at ‘image’ level to see if they are RDSRs or ESRs (Enhanced Structured Reports - not currently used but will be in the future). Any other SR series responses are deleted.

All non-SR series responses are deleted.

If CT exams were requested, and a study has CT in:

Any SR series are checked at ‘image’ level to see if they are RDSRs. If they are, all other SR and image series responses are deleted. Otherwise, if it has an ESR series, again all other SR and image series responses are deleted.

If there are no RDSR or ESR series, the other series are checked to see if they are Philips ‘Dose info’ series. If there are, other series responses are deleted.

If there are no RDSR, ESR or ‘Dose info’ series and the option to get Toshiba images has been selected, then an image level query is performed for the first image in each series. If the image is not a secondary capture, all but the first image are deleted from the image level responses and the image_level_move flag is set. If the image is a secondary capture, the whole series response is kept.

If there are no RDSR or ESR, series descriptions aren’t returned and the Toshiba option has been set, the image level query is performed as per the previous point. This process will keep the responses that might have Philips ‘Dose info’ series.

If there are no RDSR, ESR, series descriptions aren’t returned and the Toshiba option has not been set, each series with more than five images in is deleted from the series response list - the remaining ones might be Philips ‘Dose info’ series.

If SR only studies were requested:

Each series response is checked at ‘image’ level to see which type of SR it is. If is not RDSR or ESR, the study response is deleted.

If Get SR series that return nothing at image level query were requested:

It is assumed that any SR series that appears to be empty actually contains an RDSR, and the other series are dealt with as above for when an RDSR is found. If at the image level query the full data requested is returned, then the series will be processed the same whether this option is selected or not.
Duplicates processing¶

For each remaining study in the query response, the Study Instance UID is checked against the studies already in the OpenREM database.

If there is a match and the series level modality is SR (from a CT, or RF etc):

The image level response will have the SOP Instance UID - this is checked against the SOP Instance UIDs recorded with the matching study. If a match is found, the ‘image’ level response is deleted.

If there is a match and the series level modality is MG, DX or CR:

An image level query is made which will populate the image level responses with SOP Instance UIDs

Each image level response is then processed and the SOP Instance UID is checked against the SOP Instance UIDs recorded with the matching study. If a match is found, the ‘image’ level response is deleted.

Once each series level response is processed:

If the series no longer has any image level responses the series level response is deleted.

If the study no longer has any series level responses the study level response is deleted.
DICOM query summary¶

Figure 3: Go to query summary¶

Either by clicking on the “Go to query details page” when executing a query or by going to Config > DICOM query summary you can review the current and older queries, check which files were found on the remote, which studies/files were ignored and why, and review the result of importing files which were retrieved.

Figure 4: The query details page¶

By clicking on the studies of a query you can review the discovered DICOM series as well as to some extent the individual files that are part of those series. If no import tasks are shown, even though the study is marked for downloading, that probably means that the query has not been retrieved, i.e. was aborted before completion. In the example below the query was run with the setting to not ignore duplicates, therefore the study was still downloaded but then thrown away by the import.

Figure 5: The query study details page¶
Troubleshooting: openrem_qr.log¶

Note that if a query does not work as expected the first location to check should be the DICOM query summary and the Task management. However if that does not clarify the issue looking at the logs will be a good idea.

If the default logging settings haven’t been changed then there will be a log files to refer to. The default location is within your logs folder:

This file contains information about the query, the status of the remote node, the C-Find response, the analysis of the response, and the individual C-Move requests.

The following is an example of the start of the log for the following query which is run once an hour (ie some responses will already have been imported):
openrem_qr.py 2 1 -dx -f 2016-05-04 -t 2016-05-04 -e "imported"
[04/May/2016 11:30:02] INFO [remapp.netdicom.qrscu:580] qrscu script called [04/May/2016 11:30:02] INFO [remapp.netdicom.qrscu:595] Modalities are ['DX'] [04/May/2016 11:30:02] INFO [remapp.netdicom.qrscu:601] Date from: 2016-05-04 [04/May/2016 11:30:02] INFO [remapp.netdicom.qrscu:604] Date until: 2016-05-04 [04/May/2016 11:30:02] INFO [remapp.netdicom.qrscu:610] Study description exclude terms are ['imported'] [04/May/2016 11:30:03] INFO [remapp.netdicom.qrscu:267] Request association with Hospital PACS PACSAET01 (PACSEAT01 104 DICOM_QR_SCP) [04/May/2016 11:30:03] INFO [remapp.netdicom.qrscu:33] Association response received [04/May/2016 11:30:03] INFO [remapp.netdicom.qrscu:277] assoc is ... <Association(Thread-7208, started daemon 140538998306560)> [04/May/2016 11:30:03] INFO [remapp.netdicom.qrscu:280] DICOM Echo ... [04/May/2016 11:30:03] INFO [remapp.netdicom.qrscu:282] done with status Success [04/May/2016 11:30:03] INFO [remapp.netdicom.qrscu:284] DICOM FindSCU ... [04/May/2016 11:30:03] INFO [remapp.netdicom.qrscu:311] Currently querying for DX studies... [04/May/2016 11:30:03] INFO [remapp.netdicom.qrscu:33] Association response received [04/May/2016 11:30:04] INFO [remapp.netdicom.qrscu:33] Association response received [04/May/2016 11:30:04] INFO [remapp.netdicom.qrscu:33] Association response received [04/May/2016 11:30:04] INFO [remapp.netdicom.qrscu:33] Association response received [04/May/2016 11:30:05] INFO [remapp.netdicom.qrscu:33] Association response received [04/May/2016 11:30:05] INFO [remapp.netdicom.qrscu:311] Currently querying for CR studies... [04/May/2016 11:30:05] INFO [remapp.netdicom.qrscu:33] Association response received [04/May/2016 11:30:05] INFO [remapp.netdicom.qrscu:33] Association response received [04/May/2016 11:30:06] INFO [remapp.netdicom.qrscu:33] Association response received [04/May/2016 11:30:06] INFO [remapp.netdicom.qrscu:33] Association response received [04/May/2016 11:30:06] INFO [remapp.netdicom.qrscu:33] Association response received [04/May/2016 11:30:07] INFO [remapp.netdicom.qrscu:33] Association response received [04/May/2016 11:30:10] INFO [remapp.netdicom.qrscu:33] Association response received [04/May/2016 11:30:10] INFO [remapp.netdicom.qrscu:33] Association response received [04/May/2016 11:30:11] INFO [remapp.netdicom.qrscu:33] Association response received [04/May/2016 11:30:11] INFO [remapp.netdicom.qrscu:33] Association response received [04/May/2016 11:30:12] INFO [remapp.netdicom.qrscu:33] Association response received [04/May/2016 11:30:12] INFO [remapp.netdicom.qrscu:33] Association response received [04/May/2016 11:30:12] INFO [remapp.netdicom.qrscu:33] Association response received [04/May/2016 11:30:12] INFO [remapp.netdicom.qrscu:339] Checking to see if any of the 16 studies are already in the OpenREM database [04/May/2016 11:30:13] INFO [remapp.netdicom.qrscu:343] Now have 11 studies [04/May/2016 11:30:13] INFO [remapp.netdicom.qrscu:349] Deleting studies we didn't ask for [04/May/2016 11:30:13] INFO [remapp.netdicom.qrscu:358] mod is DX, mod_set is ["CR"] [04/May/2016 11:30:13] INFO [remapp.netdicom.qrscu:358] mod is CR, mod_set is ["CR"] [04/May/2016 11:30:13] INFO [remapp.netdicom.qrscu:358] mod is DX, mod_set is ["PR", "DX"] [04/May/2016 11:30:13] INFO [remapp.netdicom.qrscu:358] mod is CR, mod_set is ["PR", "DX"] [04/May/2016 11:30:13] INFO [remapp.netdicom.qrscu:358] mod is DX, mod_set is ["DX"] [04/May/2016 11:30:13] INFO [remapp.netdicom.qrscu:358] mod is CR, mod_set is ["DX"] [04/May/2016 11:30:13] INFO [remapp.netdicom.qrscu:358] mod is DX, mod_set is ["PR", "CR"] [04/May/2016 11:30:13] INFO [remapp.netdicom.qrscu:358] mod is CR, mod_set is ["PR", "CR"] [04/May/2016 11:30:13] INFO [remapp.netdicom.qrscu:367] Now have 11 studies [04/May/2016 11:30:13] INFO [remapp.netdicom.qrscu:372] Deleting series we can't use [04/May/2016 11:30:13] INFO [remapp.netdicom.qrscu:408] Now have 11 studies [04/May/2016 11:30:13] INFO [remapp.netdicom.qrscu:413] Deleting any studies that match the exclude criteria [04/May/2016 11:30:13] INFO [remapp.netdicom.qrscu:422] Now have 6 studies after deleting any containing any of [u'imported'] [04/May/2016 11:30:13] INFO [remapp.netdicom.qrscu:438] Release association [04/May/2016 11:30:13] INFO [remapp.netdicom.qrscu:499] Preparing to start move request [04/May/2016 11:30:13] INFO [remapp.netdicom.qrscu:504] Requesting move of 6 studies [04/May/2016 11:30:13] INFO [remapp.netdicom.qrscu:509] Mv: study_no 1 [04/May/2016 11:30:13] INFO [remapp.netdicom.qrscu:515] Mv: study no 1 series no 1 [04/May/2016 11:30:13] INFO [remapp.netdicom.qrscu:528] Requesting move: modality DX, study 1 (of 6) series 1 (of 1). Series contains 1 objects [04/May/2016 11:30:13] INFO [remapp.netdicom.qrscu:33] Association response received [04/May/2016 11:30:13] INFO [remapp.netdicom.qrscu:44] Move association requested [04/May/2016 11:30:18] INFO [remapp.netdicom.qrscu:53] Move association released [04/May/2016 11:30:18] INFO [remapp.netdicom.qrscu:532] _move_req launched [04/May/2016 11:30:18] INFO [remapp.netdicom.qrscu:509] Mv: study_no 2 [04/May/2016 11:30:18] INFO [remapp.netdicom.qrscu:515] Mv: study no 2 series no 1 [04/May/2016 11:30:18] INFO [remapp.netdicom.qrscu:528] Requesting move: modality DX, study 2 (of 6) series 1 (of 1). Series contains 2 objects [04/May/2016 11:30:18] INFO [remapp.netdicom.qrscu:33] Association response received [04/May/2016 11:30:19] INFO [remapp.netdicom.qrscu:44] Move association requested [04/May/2016 11:30:29] INFO [remapp.netdicom.qrscu:48] gg is Pending [04/May/2016 11:30:30] INFO [remapp.netdicom.qrscu:53] Move association released ...etc

Navigating, filtering and study details¶

Navigating the OpenREM web interface¶

Depending on your web server setup, your web interface to OpenREM will usually be at http://yourserver/openrem or if you are using the test web server then it might be at http://localhost:8000/openrem.

The home page for OpenREM should look something like this when it is populated with studies:

By selecting the links in the navigation bar at the top, you can view all of the CT, fluoroscopy, mammography, radiographic or nuclear medicine studies. Alternatively, click on any row to filter by that system.

The modality tables can be sorted by any of the columns by clicking on the column header that you wish to sort by.

If you are not logged in, clicking any of the links will bring up the log in page.

Filtering for specific studies¶

This image shows the CT studies view, available to any logged in user, filtered by entering terms in the boxes on the right hand side to show just the studies where the modality manufacturer name includes the term ‘Siemens’:

The search fields can all be used on their own or together, and they are all case insensitive ‘contains’ searches. The exception is the date field, where both from and to have to be filled in (if either are), and the format must be yyyy-mm-dd. There currently isn’t any more complex filtering available, but it does exist as issue 17 for a future release.

The last box below the filtering search boxes is the ordering preference.

CT: specifying number of event types¶

It is possible to filter for studies that have specific numbers of each acquisition type, or to only include studies have at least some (>0), or to include only studies that have zero acquisitions of a specific type.

For example, if the standard CT Abdomen on a particular scanner has two localisers and one spiral scan, then to filter for all the studies that followed this without deviation (an extra localiser or an extra series) the filters might be set to the particular Display Name and Requested Procedure, and Num. spiral events set to one and Num. localisers set to two. This can be useful for exporting a clean set of data to process for a dose audit.

Setting the number of studies displayed per page¶

The number of studies displayed per page can be controlled by changing the value selected in the Items per page drop down box, located beneath the chart options:

Viewing study details¶

By clicking on the study description link (in blue), you can see more details for an individual study:

Not all the details stored for any one study are displayed, just those thought to be most useful. If there are others you’d like to see, add an issue to the tracker.

The final field in the summary at the top is called ‘Test patient indicators?’ When studies are imported the ID and patient name fields are both ignored, but they are parsed to check if they have ‘phy’, ‘test’ or ‘qa’ in them to help exclude them from the data analysis. If they do, then this information is added to the field and is displayed both in the web interface as a Test patient indicator and in the Excel export. The name and ID themselves are not reproduced, simply the presence of one of the key words. Therefore a patient named ‘Phyliss’ would trigger this, but only ‘Phy’ would be reproduced in this field. Other fields will also help to confirm whether a study is for a real patient such as the lack of an Accession Number and an unusual patient age.

Note

For fluoroscopy the table showing details of each exposure can be sorted by clicking on the table headings.

A note on time data for fluoroscopy studies¶

On the page showing a specific fluoroscopy study there is a table that shows the details of each irradiation event in the study. This table includes a column labelled as:

Duration (ms)
Exposure time (ms)

The Duration value is the amount of time that the exposure switch or pedal was pressed (technically, this should be the time from the loading of the first x-ray pulse to the time of the trailing edge of the final pulse for that irradiation event). The Exposure time value is different: this is the total time that the x-ray beam was actually switched on for during the irradiation event. So for pulsed fluoroscopy the Exposure time will be (much) shorter than the Duration.

Near the top of each fluoroscopy study in the detail view is a table summarising the DAP, dose at reference point and duration for each irradiation type used in the study. Totals are also shown. The Total duration values in this table show the amount of time that the exposure switch or pedal was pressed.

Charts¶

From OpenREM version 1.0.0+ charts use the Plotly open source Python library.

Chart types¶

The charts below are examples of the types of chart included in OpenREM version 1.0.0+. The examples are fully interactive in the same way as the charts included in a live OpenREM system. The data contained in the example charts is synthetic.

Single-clicking on a legend entry toggles the display of that series. Double-clicking on a legend entry hides all but that series; double-click again to show all series.

Hovering the cursor over a chart causes the chart menu to appear along the top right corner. From the menu you can:

save a bitmap version of the chart
set zoom, pan and selection options
autoscale the chart
reset the axes, and also reset the regions
toggle spike lines to graphically illustrate x- and y-axis data values on hover
choose whether to show the closest data when hovering, or to compare data on hover

Bar chart of average values across categories¶

These can be configured to show mean or median data. The example below shows the median DAP for each requested procedure name containing the word “knee” across eight x-ray rooms.

When viewing a chart of this type in OpenREM, the chart data can be downloaded as a csv file by clicking the Download csv button displayed below the chart. This feature is not available for the example charts in this document.

Hovering the cursor over a bar shows the:

x-ray room name
requested procedure name
median DAP for that room and procedure
number of requests for that room and procedure

Boxplot of values across a number of categories¶

The example below shows the same data as for the bar chart above, but presented as a box plot.

Hovering the cursor over an outlier shows the:

x-ray room name
requested procedure name
DAP of the data point

Hovering the cursor over the box shows the:

maximum value
minimum value
median
1st and 3rd quartiles
lower fence: 1rd quartile - (1.5 x interquartile range)
upper fence: 3rd quartile + (1.5 x interquartile range)

Histogram bar chart of values across categories¶

The example below shows the distribution of DAP values for the knee data used in the box and bar plots above. The number of bins used in the histograms can be configured in the Additional chart options on the Config page.

Hovering the cursor over a bar shows the:

requested procedure name
x-ray room name
bin DAP range
bin DAP mid-point value
bin frequency

Bar chart of category frequency¶

The example below shows the frequency of the “knee” requested procedures for each x-ray room. The height of each bar is the total frequency for that requested procedure. Each bar is sub-divided into sections representing the number of requests for each x-ray room.

When viewing a chart of this type in OpenREM, the chart data can be downloaded as a csv file by clicking the Download csv button displayed below the chart. This feature is not available for the example charts in this document.

Hovering the cursor over a bar section shows the:

x-ray room name
requested procedure name
requested procedure frequency

Setting Grouping choice to System names in the Chart options on the modality pages groups the data by x-ray system name rather than requested procedure name, as shown below:

Scatter chart of x vs y values¶

The example below shows the average glandular dose plotted against compressed breast thickness for “MAMMOGRAM” acquisitions made on two x-ray systems.

Hovering the cursor over a bar section shows the:

x-ray room name
acquisition protocol name
compressed breast thickness
average glandular dose

Bar chart of workload¶

These show the number of studies taking place per weekday, sub-divided into hours of the day.

There is a bar per weekday. The total height of this bar is the number of studies carried out on that weekday. Each bar is sub-divided into sections representing the number of studies carried out in each of the 24 hours of that day. Each section is colour-coded according to how many studies it represents.

Hovering the cursor over a section shows you the:

x-ray room name
day of the week that the section represents
hour of the day that the section represents
number of studies that took place in that hour on that weekday in that x-ray room

Line chart of average value over time¶

These can be configured to show mean or median data. Each datapoint represents the average over a user-specified time period. This can be a day, week, month, quarter or year.

The example below shows the median DAP for “Head” requests made in four CT scanners over the course of five years.

With Grouping choice set to Series item names in the Chart options on the modality pages a sub-plot is created for each requested procedure name, each with a series per x-ray system as shown below. The Number of charts per row in the Additional chart options on the Config page was set to 2 for these example charts.

Hovering the cursor over a section shows you the:

scanner name
requested procedure name
date
average DLP value
number of requests included in the sample

Setting Grouping choice to System names in the Chart options on the modality pages changes the grouping. Now a sub-plot is created for each x-ray system, each with a series per requested procedure name, as shown below:

Bar chart of average value against another binned value across categories¶

These can be configured to show mean or median data. The example below shows the median average glandular dose from “MAMMOGRAM” protocol exposures plotted against compressed breast thickness bins. The data is from two x-ray systems.

Hovering the cursor over a section shows you the:

x-ray room name
acquisition protocol
average AGD value
number of acquisitions included in the sample
compressed breast thickness bin range

Chart options on the modality pages¶

Name	Configuration options	Notes
Average plots	Any combination of mean, median or boxplot
Time period	One of day, week, month, quarter or year	Applies to over-time charts
Grouping choice	System names or Series item names	System names groups by x-ray system Series item names groups by each category
Plot a series per system	On or off	Splits the data by x-ray system
Calculate histogram data	On or off	Calculate histograms for average bar charts
Chart sorting	One of name, frequency or value	Sort the chart data according to the selected choice
Sorting direction	Ascending or descending	Sets the sort direction
Split plots by physician	On or off	Calculate a series per physician (some fluoroscopy charts only)

Additional chart options on the Config page¶

Name	Configuration options	Notes
Number of histogram bins	Value in the range 2 - 40	Default is 10
Fixed histogram bins across sub-plots	On or off	Forces all histogram sub-plots to use the same bins
Case-insensitive categories	On or off	Category names forced to lowercase For example, “Chest PA” becomes “chest pa”
Remove category whitespace padding	On or off	Removes spaces at beginning and end of category names, replaces multiple spaces with single spaces For example, ” Chest PA ” becomes “Chest PA”
Colour map choice	One of the available matplotlib colour maps	See the available colourmaps section
Chart theme	One of Plotly, Plotly white, Plotly dark, presentation, ggplot2, Seaborn or simple white	Set the Plotly theme to use for the charts. Some example themed charts are provided below. Examples of all themes on the Plotly themes page (external link).
Number of charts per row	Value in the range 1 - 10	Sets the number of sub-plots in each row

Available colourmaps¶

Name	Swatch
Red yellow blue
Spectral
Pink yellow green
Purple green
Brown green
Purple orange
Red blue
Red grey
Yellow green blue
Yellow orange brown
Hot
Inferno
Magma
Plasma
Viridis
Cividis

Some available themes¶

The example Chart types at the top of this document use the default Plotly theme. Below are some examples of other available themes.

Plotly dark¶

Presentation¶

Simple white¶

Available CT charts¶

Chart name	Chart type
Acquisition frequency	Bar chart of acquisition protocol frequency
Acquisition DLP	Bar chart of average DLP per acquisition protocol Boxplot with data point per acquisition protocol Histograms also plotted if Calculate histogram data on
Acquisition CTDI_vol	Bar chart of average CTDI_vol per acquisition protocol Boxplot with data point per acquisition protocol Histograms also plotted if Calculate histogram data on
Acquisition DLP over time	Line chart of average DLP over time for each acquisition protocol
Acquisition CTDI_vol over time	Line chart of average CTDI_vol over time for each acquisition protocol
Acquisition DLP vs mass	Scatter chart of DLP vs patient mass for each acquisition protocol
Acquisition CTDI_vol vs mass	Scatter chart of CTDI_vol vs patient mass for each acquisition protocol
Study frequency	Bar chart of study description frequency
Study DLP	Bar chart of average DLP per study description Boxplot with data point per study description Histograms also plotted if Calculate histogram data on
Study CTDI_vol	Bar chart of average CTDI_vol per study description Boxplot with data point per study description Histograms also plotted if Calculate histogram data on
Study events	Bar chart of average number of radiation events per study description Boxplot with data point per study description Histograms also plotted if Calculate histogram data on
Study DLP over time	Line chart of average DLP over time for each study description
Study workload	Bar chart of number of studies carried out on each day of the week, with each bar sub-divided into hours of the day
Requested procedure frequency	Bar chart of requested procedure name frequency
Requested procedure DLP	Bar chart of average DLP per requested procedure name Boxplot with data point per study description Histograms also plotted if Calculate histogram data on
Requested procedure events	Bar chart of average number of radiation events per requested procedure name Boxplot with data point per study description Histograms also plotted if Calculate histogram data on
Requested procedure DLP over time	Line chart of average DLP over time for each study description

Available radiographic charts¶

Chart name	Chart type
Acquisition frequency	Bar chart of acquisition protocol frequency
Acquisition DAP	Bar chart of average DAP per acquisition protocol Boxplot with data point per acquisition protocol Histograms also plotted if Calculate histogram data on
Acquisition mAs	Bar chart of average mAs per acquisition protocol Boxplot with data point per acquisition protocol Histograms also plotted if Calculate histogram data on
Acquisition kVp	Bar chart of average kVp per acquisition protocol Boxplot with data point per acquisition protocol Histograms also plotted if Calculate histogram data on
Acquisition DAP over time	Line chart of average DAP over time for each acquisition protocol
Acquisition mAs over time	Line chart of average mAs over time for each acquisition protocol
Acquisition kVp over time	Line chart of average kVp over time for each acquisition protocol
Acquisition DAP vs mass	Scatter chart of DAP vs patient mass for each acquisition protocol
Study frequency	Bar chart of study description frequency
Study DAP	Bar chart of average DAP per study description Boxplot with data point per study description Histograms also plotted if Calculate histogram data on
Study DAP vs mass	Scatter chart of DAP vs patient mass for each study description
Study workload	Bar chart of number of studies carried out on each day of the week, with each bar sub-divided into hours of the day
Requested procedure frequency	Bar chart of requested procedure name frequency
Requested procedure DAP	Bar chart of average DAP per requested procedure name Boxplot with data point per study description Histograms also plotted if Calculate histogram data on
Requested procedure DAP vs mass	Scatter chart of DAP vs patient mass for each requested procedure name

Available fluoroscopy charts¶

Chart name	Chart type
Study frequency	Bar chart of study description frequency
Study DAP	Bar chart of average DAP per study description Boxplot with data point per study description Histograms also plotted if Calculate histogram data on
Study DAP over time	Line chart of average DAP over time for each study description
Study workload	Bar chart of number of studies carried out on each day of the week, with each bar sub-divided into hours of the day
Requested procedure frequency	Bar chart of requested procedure name frequency
Requested procedure DAP	Bar chart of average DAP per requested procedure name Boxplot with data point per study description Histograms also plotted if Calculate histogram data on
Requested procedure DAP over time	Line chart of average DAP over time for each study description

Available mammography charts¶

Chart name	Chart type
Acquisition frequency	Bar chart of acquisition protocol frequency
Acquisition AGD	Bar chart of average AGDP per acquisition protocol Boxplot with data point per acquisition protocol Histograms also plotted if Calculate histogram data on
Acquisition average AGD vs thickness	Bar chart of average AGD for each of the following nine compressed breast thickness bands: min ≤ x < 20; 20 ≤ x < 30; 30 ≤ x < 40; 40 ≤ x < 50; 50 ≤ x < 60; 60 ≤ x < 70; 70 ≤ x < 80; 80 ≤ x < 90; 90 ≤ x < max
Acquisition AGD over time	Line chart of average AGD over time for each acquisition protocol
Acquisition AGD vs thickness	Scatter chart of AGD vs compressed breast thickness for each acquisition protocol
Acquisition mAs vs thickness	Scatter chart of mAs vs compressed breast thickness for each acquisition protocol
Acquisition kVp vs thickness	Scatter chart of kVp vs compressed breast thickness for each acquisition protocol
Study workload	Bar chart of number of studies carried out on each day of the week, with each bar sub-divided into hours of the day

Available nuclear medicine charts¶

Chart name	Chart type
Study frequency	Bar chart of study description frequency
Study description workload	Bar chart of number of studies carried out on each day of the week, with each bar sub-divided into hours of the day
Injected dose per study	Bar chart or boxplot of average injected dose per study description. If calculate histogram is enabled creates a histogram of injected dose
Injected dose over time	Bar chart or boxplot of injected dose per study description over time
Injected dose over weight	Show a scatter plot of injected dose versus patient weight

Standard name mapping¶

Introduction¶

The OpenREM config menu — Figure 1: The config menu¶

The same type of examination can appear in OpenREM under a range of different requested procedure names, study descriptions or procedure names. Individual acquistions of the same type may appear under a range of different acquisition protocol names. The standard name mapping feature allows a number of differently named exams or acquisitions to be grouped into a single standard name, allowing easier analysis of data for audits and diagnostic reference level (DRL) creation (see DRL definition on the IAEA website).

If the logged in user is an OpenREM adminstrator the standard name settings option will be available on the Config drop down menu (figure 1). Clicking on this will take the administrator to the standard name mapping settings page (figure 2). From this page the administrator can enable or disable standard name mapping for the OpenREM installation. Enabling standard name mapping causes an additional column to be displayed in each modality summary page where any standard study-level names associated with each entry in the exam table are displayed (figure 3), and when viewing the details of an individual study any matching standard acquisition names are displayed in the appropriate table row (figure 4). Enabling standard name mapping also makes the standard study name and standard acquisition name charts available. The administrator can also use the standard name settings page to access the standard name internal database refresh links. Note that the use of these links is not expected to be necessary in normal OpenREM use.

Figure 2: The standard name mapping settings page¶

Figure 3: Standard study names visible in the CT summary page¶

Figure 4: Standard acquisition names visible in a study details page¶

Selecting the standard name mapping option within the Config drop down menu (figure 1) will take the user to the standard name mapping summary page(figure 5). This page shows any existing standard name mappings, which an OpenREM administrator is able to Modify or Delete using the buttons on the right hand side of each entry. A normal OpenREM user can only view standard name mappings.

Figure 5: The standard name mapping summary page¶

Creating a new standard name mapping¶

A new standard name mapping can be created by an OpenREM administrator by clicking on the Add new XX entry button, where XX corresponds to a particular modality. This takes the administrator to a screen where the new standard name is set (figure 6), and where the administrator selects the study descriptions, requested procedure names, procedure names and acquisition protocol names that they want to be included in the new standard name definition. The available items are listed in the left-hand tables. The administrator can move a required item into the right-hand table by double-clicking on an entry, or selecting an entry and then clicking on the arrow pointing to the right.

Figure 6: Adding or modifying a standard name¶

The example in figure 7 shows that head-related study descriptions and requested procedure names have been chosen for a new standard name of Head.

Once all relevant items have been transferred to the right-hand tables the Submit button at the bottom of the page must be clicked to confirm the new entry. Once a study description, requested procedure name, procedure name or acquisition protocol name has been assigned to a standard name it cannot be added to another standard name, and disappears as an option in the left-hand tables when configuring future new standard name entries.

Mapping a new standard name — Figure 7: Adding or modifying a standard name mapping¶

Modifying an existing standard name mapping¶

An existing standard name mapping can be modified by clicking the Modify button on the right-hand side of an entry in the standard name mapping summary page (figure 5). This takes the administrator to the same screen as shown in figure 7, where the chosen study descriptions, requested procedure names, procedure names and acquisition protocol names can be amended. Clicking Submit confirms the changes.

Charts¶

Charts of standard name data can be plotted in OpenREM. This can be helpful because at study-level it enables multiple study descriptions, requested procedure names and procedure names to be combined into a single data point. At acquisition level, multiple acquisition protocol names can be combined into a single data point. For example, figure 8 below shows the median DLP for a range of study descriptions. Three of the study descriptions relate to the head, and two of them relate to scans of the chest and abdomen. The three head-related descriptions have been mapped to a Head standard study name, and the two chest and abdomen descriptions have been mapped to a Chest + Abdomen standard study name, resulting in the chart shown in figure 9. The standard name mapping allows clearer visual comparison of the data per study for each hospital and piece of equipment.

Figure 8: Chart of median DLP for each study description¶

Figure 9: Chart of median DLP for each standard study name¶

Skin dose maps¶

Functionality that is available¶

Skin dose map data is calculated to the surface of a simple geometric phantom using the in-built openSkin routines (3D phantom)
The calculated doses include kVp-dependent backscatter factors and account for any copper filters using data from this paper for each irradiation event; aluminium or other filters are not considered. Where more than one kVp is stored for an irradiation event the mean kVp is calculated, excluding any zero values.
The phantom dimensions are calculated from the height and mass of the patient; defaults of 1.786 m and 73.2 kg are used when patient height and mass are not available
Data can be calculated on import to OpenREM, or on demand when a study is viewed. Calculating the skin dose map can take several minutes.
Data is recalculated automatically if the patient height or mass stored in the database differs from the values stored in the skin dose map data file. This is useful when patient size information has been imported in to OpenREM after the initial skin dose map data has been calculated
3D skin dose map data is shown graphically as a 2D image and a 3D model
The 3D model can be manipulated using a touch screen
The user can change the maximum and minimum displayed dose; alternatively, window level and width can be adjusted
A colour dose scale is shown with a selection of colour schemes
The skin dose map section can be displayed full-screen
The calculated peak skin dose, phantom dimensions, patient height, mass and orientation used for the calculations are shown in the top left hand corner of the skin dose map
Additionally the percentage of exposures that interact with the phantom and therefore contribute to the skin dose is shown as DAP percentage contributed, enhancing the interpretability of the skin dose calculation.
If skin dose map display is disabled then fluoroscopy study data can be exported in a format suitable for the stand-alone openSkin routines

The phantom consists of a cuboid with one semi-cylinder on each side (see 3D phantom section of phantom design on the openSkin website for details).

2D visualisation of the 3D data¶

2D visualisation of the 3D skin dose map data — Figure 1: 2D visualisation of the 3D data¶

This is a 2D view of the whole surface of the 3D phantom, as though the phantom surface has been peeled off and laid out flat (figure 1). The 2D visualisation includes the following features:

The skin dose at the mouse pointer is shown as a tool-tip
Moving the mouse whilst holding down the left-hand mouse button changes the window level and width of the displayed skin dose map
An overlay indicating the phantom regions and orientation can be toggled on and off. This indicates the phantom anterior, left, posterior and right sides, and also shows the superior and inferior ends (figure 2)

2D visualisation of the 3D skin dose map including phantom region overlay — Figure 2: Phantom region overlay¶

The current view can be saved as a png file

3D visualisation¶

This is a 3D view of the phantom that was used for the calculations, with the skin dose map overlaid onto the surface. The 3D visualisation includes the following features:

Moving the mouse whilst holding down the left-hand mouse button rotates the 3D model
Using the mouse wheel zooms in and out

3D visualisation of the skin dose map data — Figure 3: 3D visualisation of the data¶

A simple 3D model of a person is displayed in the bottom left corner. This is to enable the viewer to orientate themselves when viewing the 3D skin dose map
The current view can be saved as a png file

Skin dose map settings¶

There are two skin dose map options that can be set by an OpenREM administrator via the Skin dose map settings option in the Config menu:

Enable skin dose maps
Calculate skin dose maps on import
Ignore systems safelist

The first of these sets whether skin dose map data is calculated, and also switches the display of skin dose maps on or off. The second option controls whether the skin dose map data is calculated at the point when a new study is imported into OpenREM, or calculated when a user first views the details of that particular study in the OpenREM interface. The third option allows the user to enables skin dose maps for systems that are not validated for OpenSkin.

When skin dose maps are enabled:

When a user views the details of a fluoroscopy study OpenREM looks for a skin dose map pickle file on the OpenREM server in the skin_maps subfolder of MEDIA_ROOT that corresponds to the study being viewed. If found, the skin dose map data in the pickle file is loaded and displayed. The skin_maps folder is created if it does not exist
If a pickle file is not found then OpenREM calculates skin dose map data. These calculations can take some time. They are carried out in the background: an animated graphic is shown during the calculations. On successful calculation of the data the skin dose map is displayed. A pickle file containing the data is saved in the server’s skin_maps subfolder of MEDIA_ROOT. The file name is of the form skin_map_XXXX.p, where XXXX is the database primary key of the study
For subsequent views of the same study the data in the pickle file is loaded, rather than re-calculating the data, making the display of the skin dose map much quicker

When calculation on import is enabled:

OpenREM calculates the skin dose map data for a fluoroscopy study as soon as it arrives in the system
A pickle file containing the data is saved in the skin_maps subfolder of MEDIA_ROOT
Users viewing the details of a study won’t have to wait for the skin dose map data to be calculated

Exporting data to openSkin¶

If skin dose maps are disabled, and the user has export rights, the user is presented with the option of exporting the study data as a csv file that is formatted for use with a stand-alone installation of openSkin. The user must be in the detail view of the study they wish to create the exposure incidence map for, and then click on the link to create the OpenSkin export (figure 4).

Figure 4: Export from OpenREM to openSkin¶

Instructions for openSkin¶

Download the openSkin repository as a zip file from openSkin downloads. To use openSkin as a stand-alone application you need python 2.x and the pypng python library.

Extract the contents of the zip file into a folder on your computer and run python main.py from a command line and answer each question.
See phantom design for details of the 2D and 3D phantoms.
When asked for the source csv file use the one exported from OpenREM
Depending on the number of events in the export and the power of your computer the calculations can take a few minutes

Two files will be produced - a textfile called skin_dose_results.txt and a small image called skin_dose_map.png

Results text file¶

It should look something like this:

File created    : 04/21/15 17:42:45
Data file       : C:/Users/[...]/exports-2015-04-21-OpenSkinExport20150421-162805246134.csv
Phantom         : 90.0x70.0 3d phantom
Peak dose (Gy)  :                  0.50844405521
Cells > 3 Gy    :                              0
Cells > 5 Gy    :                              0
Cells > 10 Gy   :                              0

The peak dose is the peak incident dose delivered to any one-cm-square area. If any of these 1 cm² areas (referred to as cells) are above 3 Gy, then the number of cells in this category, or the two higher dose categories, are listed in the table accordingly.

Incidence map image file¶

The image file will be a small 70x90 px PNG image if you used the 3D phantom, or 150 x 50 px PNG if you used the 2D phantom. With both, the head end of the table is on the left.

The image is scaled so that black is 0 Gy and white is 10 Gy. For most studies, this results in an incidence map that is largely black. However, if you use GIMP or ImageJ or similar to increase the contrast, you will find that the required map is there.

A native and ‘colour equalised’ version of the same export are shown below:

Limitations¶

Skin dose map calculations do not currently work for all systems. Siemens Artis Zee data is known to work. If skin dose maps do not work for your systems then please let us know via the OpenREM Google Group.

openSkin is yet to be validated independently - if this is something you want to do, please do go ahead and feed back your findings to Jonathan Cole at jacole.

Exporting study information¶

Exporting to csv and xlsx sheets¶

If you are logged in as a user in the exportgroup or the admingroup, the export links will be available near the top of the modality filter pages in the OpenREM interface.

For each modality you can export to a single-sheet csv file or a multi-sheet xlsx file. In addition, there is an export tailored to the NHSBSP dose audits requirements.

If you are logged in as a user in the pidgroup you will also have a choice of exporting with patient name and/or patient ID information included in the export (if any is recorded in the database). See Patient identifiable data for more details.

The xlsx export has multiple sheets. The first sheet contains a summary of all the study descriptions, requested procedures and series protocol names contained in the export:

This information is useful for seeing what data is in the spreadsheet, and can also be used to prioritise which studies or protocols to analyse based on frequency.

The second sheet of the exported file lists all the studies, with each study taking one line and each series in the study displayed in the columns to the right.

The remainder of the file has one sheet per series protocol name. Each series is listed one per line. If a single study has more than one series with the same protocol name, then the same study will appear on more than one line.

Fluoroscopy exports¶

Fluoroscopy csv exports only report study level information — this includes summary dose information for fluoroscopy exposures and acquisition exposures, but not information about individual exposures.

Fluoroscopy xlsx exports contain the following sheets:

Summary sheet
All data sheet with groups of exposures
One sheet per acquisition protocol with one row per exposure including all the details of that exposure.

Exposures are considered similar and put in the same group if they are, relative to the first exposure of a group:

same plane (for bi-plane systems)
same protocol
same field size (mag, not collimation)
same pulse rate
same filter material and thickness
within 5° in both directions (primary and secondary)
of the same ‘event type’ (Fluoroscopy, Stationary Acquisition, Stepping Acquisition, Rotational Acquisition)

The minimum, maximum and mean of all the remaining factors are presented for each group along with the common factors. Where a factor is not available in the source RDSR, that factor is not considered.

The grouping process for the all data sheet takes a lot of time compared to the other exports. However, we hope that this is a useful way of comprehending the study. Other modalities have all the series for any one study detailed in full on one long row — this is not possible when one study might have 400 exposures!

The majority of systems report kV, mA and pulse width information as a mean value per exposure. Some systems report this information on a per pulse basis instead. In this circumstance, in the web interface you will see the list of pulses, but in the export the mean value (after excluding any zero values) is calculated first and this value is then used.

Nuclear medicine exports¶

Nuclear medicine does not have a concept like different irradiation events. Therefore in contrast to the other modalities it the summary dose information does not show protocols, and the excel export has individual sheets per study description instead of per protocol names.

Exports page¶

Clicking the link for an export redirects you to the Exports page, which you can also get to using the link at the top right of the navigation bar:

Whilst an export is being processed, it will be listed in the first table at the top. The current status is displayed to indicate export progress, and is updated every two seconds. You can stop an export early by using the abort button; you will not be able to download anything in this instance.

Once a study is complete a new table of recently completed exams is created and you will be able to download the file.

When the export is no longer needed, it can be deleted from the server by ticking the delete checkbox and clicking the delete button at the bottom:

Specific modality export information¶

NHSBSP dose audits¶

This export is specific to the UK NHS Breast Screening Programme and generates the source data in the format required for the dose audit database developed by the National Co-ordinating Centre for the Physics of Mammography.

It has been modified to clean up the data to remove exposures that are unlikely to be wanted in the submitted data, such as exposures with any of the following in the protocol name:

scout, postclip, prefire, biopsy, postfire, stereo, specimin, artefact

The view codes have been modified to match the NCCPM convention, i.e. medio-lateral oblique is recorded as OB instead of MLO. The other codes are mapped to the ACR MQCM 1999 Equivalent code.

Each patient is numbered from starting from 1. Each view for any one patient has a unique view code, so if a second cranio-caudal exposure is made to the left breast the view codes will be LCC and LCC2.

The survey number is left as 1. This needs to be modified as appropriate. The easiest way to do this in Excel is to change the first two or three rows, select those cells that have been changed, then double click on the bottom-right corner of the selection box to copy-down the value to all the remaining cells below.

The data can then be copied and pasted into the NCCPM database.

If there are a mixture of 2D and tomography exposures, providing you can separate them by virtue of the filter used, then you should further prepare the data as follows:

Copy the sheet to a new sheet
In the first sheet, filter for the target and filter combination used for used for the tomographic exposures and delete those rows.
In the second sheet, filter for the target and filter combinations used for 2D exposures and delete those rows.
Change the survey number on the 2D sheet and the the survey number on the tomographic sheet as appropriate, with the tomographic survey number bing one more than the 2D survey number.

Where patients have had both 2D and tomographic exposures in the same study, NCCPM will be able to match them up as they will have the same patient number in both surveys.

PHE 2019 CT survey and IPEM/PHE 2019 paediatric CT survey¶

This export is specific to the UK Public Health England (PHE) CT dose survey and exports the data in the correct format to copy and paste into the spreadsheet provided by PHE. More information about the survey and copies of the data collection spreadsheet can be found on the CT User Group (CTUG) website. The same export function is also suitable for the UK Paediatric CT Dose Survey launched by the IPEM paediatric optimisation working party in collaboration with PHE. The spreadsheet and instructions for this survey can also be found on the CTUG website.

The introduction and guidance tabs of the PHE data collection spreadsheet should be read and the ‘Your details’ sheet completed. Then the ‘Patient and Protocol data 1’ sheet should be copied and renamed appropriately for each protocol and scanner combination that you will be submitting. The first 142 rows of each sheet should be filled in manually with all the details for that protocol, though looking at study data in OpenREM may help to answer some of the questions.

The CT studies should then be filtered in OpenREM; by date (ideally previous 12 months, no older than 2017), by scanner (each scanner and protocol combination should be a new sheet), by age of (minimum of 16 for the PHE adult survey), and by study description (or combination of factors to specify a particular protocol).

Finally the studies should be filtered to have exactly the right number of each type of acquisition for that protocol. This might be one spiral, one localiser and two stationary (bolus tracking) acquisitions for example. Localisers do not appear in the export so are less important to specify, but more localisers than usual might indicate a deviation from the standard protocol.

The export can then be started and monitored in the normal way by clicking on the ‘PHE 2019 survey’ button. The resulting export will be in xlsx format, with one header row. The data from row 2 onwards can be copied and pasted directly into row 150 (152 for paed survey) onwards of the Patient and Protocol sheet of the adult PHE data collection spreadsheet. The adult survey starts in column A, the paediatric survey starts in column B. Column AL (AM for paed survey) is for patient comments, and OpenREM uses this cell to record the series types that have been exported for each study. This can therefore be used to double check the data is as you expect it to be. If the protocol has more than four series excluding localisers, the data is continued in the same format from column AM (AN for paed) onwards.

PHE 2019 X-ray, fluoroscopy and interventional radiology survey¶

As with the PHE and IPEM CT surveys, the PHE exports on the radiography and fluoroscopy modality filter pages are designed to paste directly into templates provided by PHE via the Medical-Physics-Engineering JiscMail e-mail list or available directly from PHE.

The spreadsheets provided are:

Planar Radiography Survey:

PHE_Dose_PR_Patient_xxxxx.xlsx Individual patient records - OpenREM export is designed for this

PHE_Dose_PR_Patient_X26_xxxxx.xlsx Individual patient records, specifically for skeletal surveys

PHE_Dose_PR_System_xxxxx.xlsx System mean and median - OpenREM does not support this summary format directly

IR and Fluoroscopy Survey:

PHE_Dose_IR_Fluoro_Patient_xxxxx.xlsx Individual patient records - OpenREM export is designed for this

PHE_Dose_IR_Fluoro_System_xxxxx.xlsx System mean and median - OpenREM does not support this summary format directly

As with the CT surveys, care should be taken to read and fill in the instructions and questions asked in the templates.

For the radiography survey, there are two types of studies asked for:

Single-projection studies - for example the sheet Abdomen_AP_DAP_by_record
Multi-projection studies - for example the sheet Abdomen_exam_DAP_by_record

For studies in the former category, select your data as appropriate on the Radiography filter page, making use of the ‘Num. events total’ filter to ensure all selected studies have just one exposure. Then use the ‘PHE 2019 Survey: Projection’ button to export the data. If any exams do have more than one exposure, the export will continue with a warning, and only include the first exposure of each study in the export.

For multi-projection studies, filter as before, setting the ‘Num. events total’ filter if the exam normally has a specific number of views, and export using the ‘PHE 2019 Survey: Study’ button. If any of the studies have more than six exposures, the export will automatically format the data to suit the template designed for skeletal studies. This allows for up to 20 exposures. However, you will need to request the ‘bespoke’ template from PHE as it was not distributed in the original email!

The exported spreadsheet has a header row at the top. Copy from the second row onward and paste into the relevant sheet in the PHE template in row 7.

For The fluoroscopy survey, select the studies as appropriate and use the ‘PHE 2019 Survey’ button to export the data. The resulting data should be copied into the PHE_Dose_IR_Fluoro_Patient_xxxxx.xlsx as follows:

Select the exported data from column A through to column AQ, row 1 though to the last row of the exported data

Select the cell A1 and choose the full Paste Special menu - Ctrl+Alt+V or right-click Paste Special -> Paste Special. Then select Paste ‘Formulas’ and ‘Skip Blanks’.

This will paste the correct DAP and time units into row 4 along with the exported data into row 7 onwards. The DAP units exported using the radiography exports (cGy·cm²) and fluoroscopy (Gy·m²) may not correspond to the units normally used on your system, but the exported values will be correct for the units stated.

Opening csv exports in Excel¶

If the export contains non-ASCII characters, then Microsoft Excel is unlikely to display them correctly by default. This issue does not occur with Libre Office which defaults to UTF-8 – behaviour with other applications will vary.

To correctly render characters in csv files with Excel, you will need to follow the following procedure:

Open Excel.
On the Data tab of the ribbon interface, select From Text in the Get External Data section.
Select your exported csv file and click Import
Ensure that Data Type Delimited is selected.
Change the File origin from to 65001 : Unicode (UTF-8) – the easiest way to find it is to scroll right to the bottom of the list, then move up one.
Click Next >
Change the delimiter to just Comma
Either click Finish or Next > if you want to further customise the import.

Troubleshooting¶

General Docker troubleshooting¶

All commands should be run from the folder where docker-compose.yml is.

To list the active containers:

$ docker-compose ps

To list active containers running anywhere on the system:

$ docker ps

To start the containers and detach (so you get the command prompt back instead of seeing all the logging):

$ docker-compose up -d

To stop the containers:

$ docker-compose down

To see logs of all the containers in follow mode (-f) and with timestamps (-t):

$ docker-compose logs -ft

To see logs of just one container in follow mode - use the service name from the docker-compose.yml file, choose from openrem, db (PostgreSQL), nginx (web server), orthanc_1 (DICOM server):

$ docker-compose logs -f orthanc_1

Other Docker errors¶

Errors at docker-compose up¶

Cannot start service nginx¶

Error message when running docker-compose up -d (example from Windows):

ERROR: for nginx Cannot start service nginx: driver failed programming external connectivity on endpoint
openrem-nginx (...): Error starting userland proxy: listen tcp 0.0.0.0:80: bind: An attempt was made to access a
socket in a way forbidden by its access permissions.
ERROR: Encountered errors while bringing up the project.

This error indicates port 80 is not available for Docker/OpenREM. Try:

Shutting down other web servers, such as IIS
Using an alternative port temporarily for OpenREM:
- Edit the docker-compose.yml file
- Find the section that includes
nginx: ports: - 80:80
- Change the external facing port to a high number, for example:
nginx: ports: - 8080:80

Now stop and start the containers again:

$ docker-compose down
$ docker-compose up -d

If there are no errors, check that the containers are up and which ports are in use:

$ docker-compose ps

Connection was reset, Orthanc restarting¶

After installation, browsing to the webservice reports “The connection was reset”.
docker-compose ps reports:

openrem-orthanc-1                      /docker-entrypoint.sh /tmp ...   Restarting

Orthanc Docker logs include:

openrem-orthanc-1 | E1208 12:51:29.599961 OrthancException.cpp:57] The specified path does not point to a regular file: The path does not point to a regular file: /etc/share/orthanc/scripts/openrem_orthanc_config_docker.lua
openrem-orthanc-1 | E1208 12:51:29.600051 ServerIndex.cpp:706] INTERNAL ERROR: ServerIndex::Stop() should be invoked manually to avoid mess in the destruction order!

This might indicate that the bind mounts have not worked. This might be due to SELinux, particularly if you are using Red Hat or Fedora or related distributions.

See Docker SELinux configuration

OpenREM log files¶

Log file location, naming and verbosity were configured in the .env.prod configuration - see the Docker env configuration configuration docs for details.

The openrem.log has general logging information, the other two are specific to the DICOM store and DICOM query-retrieve functions if you are making use of them.

You can increase the verbosity of the log files by changing the log ‘level’ to DEBUG, or you can decrease the verbosity to WARNING, ERROR, or CRITICAL. The default is INFO.

To list the OpenREM log folder (with details, sorted with newest at the bottom, ‘human’ file sizes):

$ docker-compose exec openrem ls -rlth /logs

To review the openrem.log file for example:

$ docker-compose exec openrem more /logs/openrem.log

Older stuff¶

Server 500 errors¶

Turn on debug mode

This will render a debug report in the browser - usually revealing the problem.

Docker installs¶

Edit the .env.prod file. Find the following line and change it from 0 to 1:

DEBUG=1

Restart the containers using a command line in the folder containing your installation. This might be enough:

docker-compose up -d

If the webserver fails, then restart all the containers:

docker-compose down
docker-compose up -d

Non-Docker installs¶

Locate and edit your local_settings file

nano /var/dose/veopenrem3/lib/python3.8/site-packages/openrem/local_settings.py

Find the following line and make it active:

DEBUG = True

Restart the web service:

sudo systemctl reload openrem-gunicorn.service

Returning to normal mode¶

You should always disable debug mode when you have fixed the error. If you leave debug mode in place, the system is likely to run out of memory as database queries are cached in this mode.

Docker:

Edit .env.prod to set DEBUG=0
Restart docker-compose

Non-docker:

Edit local_settings.py again to comment out the DEBUG line (add a # to the start) or set it to False
Reload the web service

Fixing accumulated AGD and laterality for Hologic DBT¶

The code for extracting dose related information from Hologic digital breast tomosynthesis proprietary projection images object used an incorrect tag to extract the laterality of the image in releases before 0.8.0 in June 2018. As a result the accumulated AGD code didn’t work, so the accumulated AGD cell on the mammography summary sheets remained blank.

Releases between 0.8.0 and 0.10.0 had instructions on how to rectify this for existing studies in the database, but these instructions are not suitable for version 1.0 and later and therefore these instructions have been removed.

If you have a modality where every study has one event (usually CT), review

If planar X-ray studies are appearing in fluoroscopy or vice-versa, review

Display names and user-defined modalities

For DICOM networking:

Troubleshooting: openrem_qr.log for query retrieve
Troubleshooting: openrem_store.log for DICOM store

For task management:

Task management

Log files¶

Starting again!¶

If for any reason you want to start again with the database, then this is how you might do it:

SLQite3 database¶

Delete or rename your existing database file (location will be described in your local_settings.py file)
? ref to database creation here, if SQLite3 features anywhere?

Any database¶

These instructions will also allow you to keep any user settings if you use an SQLite3 database.

In a shell/command window, move into the openrem folder:

Ubuntu linux: cd /usr/local/lib/python2.7/dist-packages/openrem/
Other linux: cd /usr/lib/python2.7/site-packages/openrem/
Linux virtualenv: cd virtualenvfolder/lib/python2.7/site-packages/openrem/
Windows: cd C:\Python27\Lib\site-packages\openrem\
Windows virtualenv: cd virtualenvfolder\Lib\site-packages\openrem\

Run the django python shell:

$ python manage.py shell

>>> from remapp.models import GeneralStudyModuleAttr
>>> a = GeneralStudyModuleAttr.objects.all()
>>> a.count()  # Just to see that we are doing something!
53423

And if you are sure you want to delete all the studies…

>>> a.delete()
>>> a.count()
0

>>> exit()

Developers¶

Contents:

Creating a development environment¶

Install Python 3.6+, preferably Python 3.8: check your Linux distribution docs to see how to install a particular version; for Windows go to https://www.python.org/downloads/. Check “Add Python 3.8 to PATH” during installation.

Install git: sudo apt install git or equivalent on Linux; for Windows go to https://git-scm.com/download/win

Recommended - install an integrated development environment such as PyCharm or Visual Studio Code (many others are available).

Recommended - install PostgreSQL database

Check out git repo¶

Either clone the main OpenREM repository, or fork it first and then clone that (adapt the command accordingly):

$ git clone https://bitbucket.org/openrem/openrem.git

The OpenREM source code will now be in a folder called openrem. If you wish to specify the folder, you could do this by adding the folder name to the clone command.

Create Python virtual environment¶

Linux - install the Python package venv using pip (Windows users, venv should have been installed with Python automatically):

$ sudo apt install python3-venv

Then create the Python virtual environment in a folder called openrem-venv (change as required):

Linux:

$ python3.8 -m venv openrem-venv
$ . openrem-venv/bin/activate

Windows PowerShell (for cmd.exe substitute Activate.ps1 with activate.bat)

PS C:\Path\To\Coding Folder> C:\Python38\python -m venv openrem-venv
PS C:\Path\To\Coding Folder> .\openrem-venv\Scripts\Activate.ps1

For users of VS Code, it can be useful to create the virtual environment in a folder called .venv within your project folder (where you checked out the git repo), then VS Code will find it automatically. If you are using PyCharm you can click on the Python interpreter at the bottom right and click ‘Add Interpreter’.

Install the Python libraries¶

Assumes:

git repository is in a sub-folder called openrem - change as necessary
venv is activated

$ pip install -e openrem/

Setup OpenREM¶

You’ll need a basic configuration of OpenREM to run any code locally - copy the openremproject/local_settings.py.example to openremproject/local_settings.py and set a path for a SQLite database etc.

To use PosgreSQL instead of SQLite3, set up a user in pgAdmin 4 on Windows, and an empty database with the same user as owner, or use the Database and OpenREM config instructions on Linux.

Run test webserver¶

To see the changes you have made with the web interface, you can use the built-in Django webserver:

python manage.py runserver --insecure

In a web browser on the same computer, go to http://localhost:8000/ - you should now see the message about creating users.

Get coding¶

Create a branch in the git repository, and start making your changes, adding your features etc!

When you are done, push it back to Bitbucket and send in a pull request! Ideally, try and use the refs #123 syntax in commit messages to reference the issue on Bitbucket you are working on.

Running the test suite¶

TODO: Update for Python 3, OpenREM 1.0

Code formatting and tests¶

Steps before pushing to Bitbucket. Commands assume you are in the root directory of the git repository, at the same level as README.rst and requirements.txt etc, and that you have activated a virtualenv with the project requirements installed (pip install -e .) plus Black (pip install black)

Run black against the code:

$ black --exclude stuff/ .

Check the changes made, edit where necessary. Black is an opinionated Python formatter and in general OpenREM code should be subjected to it. The flake8 tests are tuned to agree with Black.

Run the Django tests:

$ python openrem/manage.py test remapp --parallel

old stuff to be updated

Preparation¶

Install the dependencies and OpenREM¶

OpenREM is a Django application, and therefore we use Django’s test-execution framework to test OpenREM.

The first thing to do is to create a local copy of the git repository, then install all of OpenREM’s dependencies in a virtualenv.

You will need python, pip, git and virtualenv installed - see the links on the : doc : install-prep docs for the latter, but you might try pip install virtualenv.

mkdir openremrepo
git clone https://bitbucket.org/openrem/openrem.git openremrepo

Now create the virtualenv:

mkdir veOpenREM
virtualenv veOpenREM
. veOpenREM/bin/activate  # Linux
veOpenREM\Scripts\activate  # Windows

At this stage there should be a (veOpenREM) prefix to our prompt telling us the virtualenv is activated.

Now install the dependencies:

pip install -e openremrepo/
pip install https://bitbucket.org/edmcdonagh/pynetdicom/get/default.tar.gz#egg=pynetdicom-0.8.2b2

In the future it might be necessary to install numpy too for testing.

Configure OpenREM¶

Rename and configure openremproject/local_settings.py.example and openremproject/wsgi.py.example as per the : doc :install docs.

Create a database following the same : doc :install instructions.

Run the tests!¶

Making sure the virtualenv is activated, move to openremrepo/openrem and run:

python manage.py test remapp

All the tests that exit in openrem/remapp/tests/ will now be run.

Creating test versions of production systems¶

If you wish to create a duplicate install to test upgrades etc, refer to Database restore and the preceding text regarding making backups.

Translating OpenREM strings¶

OpenREM’s primary language is British English (en_GB). Users and developers with knowledge of other languages can create translations of the interface strings, export file strings and the documentation. These will then be exposed when the web browser language is set to match the new translation language (OpenREM interface) or when the language is selected for the documentation.

A web-based service for managing translations has kindly been provided to OpenREM by Weblate. Their hosting is free to OpenREM, and they welcome donations.

Translators¶

Create an account at https://hosted.weblate.org
The OpenREM project is at https://hosted.weblate.org/projects/openrem/
Each page in the Read The Docs documentation (https://docs.openrem.org) is a separate ‘component’ in Weblate, and they have been named ‘RTD document name’. The web interface strings are all in one ‘component’.
Choose a component, and on the next page you can select one of the existing translations which you can review, edit and propose new translation strings.
Once approved, they will be merged in by developers

Creating new language translations¶

At the component level, you will see an option to create a new translation. This might need to be done for each component individually.

Code syntax in strings¶

Be careful not to edit code syntax within strings. For example, Python code might be:

Writing study {row} of {numrows} to All data sheet and individual protocol sheets

This is translated into Norwegian Bokmål as:

Skriver studie av {row} av {numrows} til alle datablad og individuelle protokollblader

Notice that the {} and their contents is unchanged - but may be moved around within the sentence to produce the correct grammar for the language being used.

Similarly with Django HTML template strings:

Number in last %(day_delta)s days

becomes:

Antall de siste %(day_delta)s dagene

It is essential that the %()s as well as the string inside the brackets stay intact.

For the RTD translations, there will be Sphinx codes that should be left untranslated, for example:

:ref:`genindex`

Developers¶

Install pre-requisites¶

gettext

Linux: sudo apt install gettext or equivalent for your distribution. For Windows: download a precompiled binary installer

sphinx-intl

Activate development environment - see Creating a development environment for details - and add the sphinx packages:

$ pip install sphinx
$ pip install sphinx-intl
$ pip install sphinx-argparse
$ pip install sphinx_issues
$ pip install sphinx_copybutton

Update .pot and .po files¶

Activate the development environment and move to the root of the OpenREM repository - with the docs folder and openrem folder etc:

$ cd docs/
$ mkdir _static
$ sphinx-build -b gettext . _build/gettext/
$ sphinx-intl update -p _build/gettext/
$ cd ../openrem/
$ django-admin makemessages -a --keep-pot

Adding new interface strings for translation¶

Please refer to https://docs.djangoproject.com/en/2.2/topics/i18n/translation/ for instructions.

In brief, the following will help get you started, but does not cover lazy translations, plurals and many other things!

All the Sphinx/Read The Docs strings are translatable - if a page does not appear in Weblate that is because it has not been configured as a component there yet.

Python code¶

First, import gettext from Django:

from django.utils.translation import gettext as _

Then wrap strings to be translated with _() so

query.stage = "Checking to see if any response studies are already in the OpenREM database"

becomes

query.stage = _(
    "Checking to see if any response studies are already in the OpenREM database"
)

The same is done for strings that contain variables. Unfortunately gettext cannot work with f-strings so we are stuck with .format() instead. It is easier to understand how to translate the text though if we use named variables rather than position based ones, like this:

query.stage = _("Filter at {level} level on {filter_name} that {filter_type} {filter_list}".format(
    level=level, filter_name=filter_name, filter_type=filter_type, filter_list=filter_list
))

Remember we cannot assume the grammar of the translated string so try and pass the whole sentence or paragraph to be translated.

Template code¶

Add the following at the top of the template file, just after any extends code:

{% load i18n %}

This can be done with inline translations and block translations. For inline,

<th style="width:25%">System name</th>

becomes

<th style="width:25%">{% trans "System name" %}</th>

If there are variables, a block translation is required, for example:

{% if home_config.display_workload_stats %}
    <th style="width:12.5%">{% blocktrans with home_config.day_delta_a as day_delta trimmed %}
        Number in last {{ day_delta }} days{% endblocktrans %}</th>
    <th style="width:12.5%">{% blocktrans with home_config.day_delta_b as day_delta trimmed %}
        Number in last {{ day_delta }} days{% endblocktrans %}</th>
{% endif %}

Comments can be added to aid translators, for example:

{# Translators: Number of studies in DB listed above home-page table. No final full-stop in English due to a.m./p.m. #}
{% now "DATETIME_FORMAT" as current_time %}
{% blocktrans with total_studies=homedata.total trimmed%}
    There are {{ total_studies }} studies in this database. Page last refreshed on {{ current_time }}
{% endblocktrans %}

Making use of updated strings on local system¶

Specify the language to build for Sphinx docs, eg for German:

$ sphinx-build -b html -D language=de . _build/html/de

For Django strings:

$ django-admin compilemessages

Incorporating translations into main repo¶

In the git repository:

$ git remote add weblate https://hosted.weblate.org/git/openrem/web-interface/

Checkout the weblate\develop branch as a new local branch
Push the branch to Bitbucket
Create a pull request to develop

DICOM import modules¶

RDSR module¶

Ultimately this should be the only module required as it deals with all Radiation Dose Structured Reports. This is used for CT, fluoroscopy, mammography, digital radiography and nuclear medicine.

openrem.remapp.extractors.rdsr.rdsr(rdsr_file)¶

Extract radiation dose related data from DICOM Radiation SR objects.

Parameters: rdsr_file (str.) – relative or absolute path to Radiation Dose Structured Report.

Mammography module¶

Mammography is interesting in that all the information required for dose audit is contained in the image header, including patient ‘size’, ie thickness. However the disadvantage over an RSDR is the requirement to process each individual image rather than a single report for the study, which would also capture any rejected images.

openrem.remapp.extractors.mam.mam(mg_file)¶

Extract radiation dose structured report related data from mammography images

Parameters: mg_file (str.) – relative or absolute path to mammography DICOM image file.

CR and DR module¶

In practice this is only useful for DR modalities, but most of them use the CR IOD instead of the DX one, so both are catered for. This module makes use of the image headers much like the mammography module.

openrem.remapp.extractors.dx.dx(dig_file)¶

Extract radiation dose structured report related data from DX radiographic images

Parameters: filename (str.) – relative or absolute path to DICOM DX radiographic image file.

NM Image module¶

This has the abilty to read information from the DICOM Headers of PET and NM images. In contrast to the other import modules this may actually complement the data read from an RRDSR, because not all relevant data is included there.

openrem.remapp.extractors.nm_image.nm_image(filename: str)¶

Extract radiation dose related data from DICOM PET/NM-Image.

Parameters: filename – relative or absolute path to PET/NM-Image.

CT non-standard modules¶

Philips CT dose info reports¶

These have all the information that could be derived from the images also held in the DICOM header information, making harvesting relatively easy. Used where RDSR is not available from older Philips systems.

openrem.remapp.extractors.ct_philips.ct_philips(philips_file)¶

Extract radiation dose structured report related data from Philips CT dose report images

Parameters: filename (str.) – relative or absolute path to Philips CT dose report DICOM image file.

Tested with:

Philips Gemini TF PET-CT v2.3.0
Brilliance BigBore v3.5.4.17001.

Toshiba dose summary and images¶

OpenREM can harvest information from older Toshiba CT systems that create dose summary images but cannot create RDSR objects by using a combination of tools to create an RDSR that can then be imported in the normal manner. This extractor requires that the Offis DICOM toolkit, java.exe and pixelmed.jar are available to the system.

openrem.remapp.extractors.ct_toshiba.ct_toshiba(folder_name)¶

Function to create radiation dose structured reports from a folder of dose images.

Parameters: folder_name – Path to folder containing Toshiba DICOM objects - dose summary and images

Non-DICOM import modules¶

Patient height and weight csv import module¶

This module enables a csv file to be parsed and the height and weight information extracted and added to existing studies in the OpenREM database. An example may be a csv extract from a RIS or EPR system.

There needs to be a common unique identifier for the exam - currently this is limited to accession number or study instance UID.

openrem.remapp.extractors.ptsizecsv2db.csv2db()¶

Import patient height and weight data from csv RIS exports. Called from openrem_ptsizecsv.py script

Parameters: args – sys.argv from the command line call

Example:

openrem_ptsizecsv.py -s MyRISExport.csv StudyInstanceUID height weight

openrem.remapp.extractors.ptsizecsv2db.websizeimport(csv_pk=None)¶

Task to import patient size data from the OpenREM web interface.

Parameters: csv_pk – Database index key for the import record, containing the path to the import csv file and the field header details.

Export from database¶

Multi-sheet Microsoft Excel XLSX exports¶

This export has a summary sheet of all the requested and performed protocols and the series protocols. The next sheet has all studies on, one study per line, with the series stretching off to the right. The remaining sheets are specific to each series protocol, in alphabetical order, with one series per line. If one study has three series with the same protocol name, each one has a line of its own. For NM this is not true as protocol names do not exist, sheets are generated by study description instead.

remapp.exports.rf_export.rfxlsx(filterdict, pid=False, name=None, patid=None, user=None)¶

Export filtered RF database data to multi-sheet Microsoft XSLX files.

Parameters

filterdict – Queryset of studies to export
pid – does the user have patient identifiable data permission
name – has patient name been selected for export
patid – has patient ID been selected for export
user – User that has started the export

Returns

Saves xlsx file into Media directory for user to download

remapp.exports.ct_export.ctxlsx(filterdict, pid=False, name=None, patid=None, user=None)¶

Export filtered CT database data to multi-sheet Microsoft XSLX files

Parameters

filterdict – Queryset of studies to export
pid – does the user have patient identifiable data permission
name – has patient name been selected for export
patid – has patient ID been selected for export
user – User that has started the export

Returns

Saves xlsx file into Media directory for user to download

remapp.exports.dx_export.dxxlsx(filterdict, pid=False, name=None, patid=None, user=None)¶

Export filtered DX and CR database data to multi-sheet Microsoft XSLX files.

Parameters

filterdict – Queryset of studies to export
pid – does the user have patient identifiable data permission
name – has patient name been selected for export
patid – has patient ID been selected for export
user – User that has started the export

Returns

Saves xlsx file into Media directory for user to download

remapp.exports.mg_export.exportMG2excel(filterdict, pid=False, name=None, patid=None, user=None, xlsx=False)¶

Export filtered mammography database data to a single-sheet CSV file or a multi sheet xlsx file.

Parameters

filterdict – Queryset of studies to export
pid – does the user have patient identifiable data permission
name – has patient name been selected for export
patid – has patient ID been selected for export
user – User that has started the export
xlsx – Whether to export a single sheet csv or a multi sheet xlsx

Returns

Saves csv file into Media directory for user to download

remapp.exports.nm_export.exportNM2excel(filterdict, pid=False, name=None, patid=None, user=None)¶

Parameters

filterdict – Queryset of studies to export
pid – does the user have patient identifiable data permission
name – has patient name been selected for export
patid – has patient ID been selected for export
user – User that has started the export

Returns

Saves xlsx file into Media directory for user to download

Single sheet CSV exports¶

remapp.exports.rf_export.exportFL2excel(filterdict, pid=False, name=None, patid=None, user=None)¶

Export filtered fluoro database data to a single-sheet CSV file.

Parameters

filterdict – Queryset of studies to export
pid – does the user have patient identifiable data permission
name – has patient name been selected for export
patid – has patient ID been selected for export
user – User that has started the export

Returns

Saves csv file into Media directory for user to download

remapp.exports.ct_export.ct_csv(filterdict, pid=False, name=None, patid=None, user=None)¶

Export filtered CT database data to a single-sheet CSV file.

Parameters

filterdict – Queryset of studies to export
pid – does the user have patient identifiable data permission
name – has patient name been selected for export
patid – has patient ID been selected for export
user – User that has started the export

Returns

Saves csv file into Media directory for user to download

remapp.exports.dx_export.exportDX2excel(filterdict, pid=False, name=None, patid=None, user=None)¶

Export filtered DX database data to a single-sheet CSV file.

Parameters

filterdict – Queryset of studies to export
pid – does the user have patient identifiable data permission
name – has patient name been selected for export
patid – has patient ID been selected for export
user – User that has started the export

Returns

Saves csv file into Media directory for user to download

remapp.exports.nm_export.exportNM2csv(filterdict, pid=False, name=None, patid=None, user=None)¶

Parameters

filterdict – Queryset of studies to export
pid – does the user have patient identifiable data permission
name – has patient name been selected for export
patid – has patient ID been selected for export
user – User that has started the export

Returns

Saves csv file Media directory for user to download

Specialised csv exports - NHSBSP formatted mammography export¶

remapp.exports.mg_csv_nhsbsp.mg_csv_nhsbsp(filterdict, user=None)¶

Export filtered mammography database data to a NHSBSP formatted single-sheet CSV file.

Parameters: filterdict (dict) – Dictionary of query parameters from the mammo filtered page URL.
Returns: None - file is saved to disk and location is stored in database

Tools and helper modules¶

Get values¶

Tiny modules to reduce repetition in the main code when extracting information from DICOM headers using pydicom.

openrem.remapp.tools.get_values.get_keys_by_value(dict_of_elements, value_to_find)¶: Get a list of keys from a dictionary which have the given value :param dict_of_elements: a dictionary of elements :param value_to_find: the value to look for in the dictionary :return: list of key names matching the given value

openrem.remapp.tools.get_values.get_or_create_cid(codevalue, codemeaning)¶

Create a code_value code_meaning pair entry in the ContextID table if it doesn’t already exist.

Parameters

codevalue (int.) – Code value as defined in the DICOM standard part 16
codemeaning – Code meaning as defined in the DICOM standard part 16

Returns

ContextID entry for code value passed

openrem.remapp.tools.get_values.get_seq_code_meaning(sequence, dataset)¶

From a DICOM sequence, get the code meaning.

Parameters

sequence (DICOM keyword, no spaces or plural as per dictionary.) – DICOM sequence name.
dataset (DICOM dataset) – The DICOM dataset containing the sequence.

Returns

str. – code meaning

openrem.remapp.tools.get_values.get_seq_code_value(sequence, dataset)¶

From a DICOM sequence, get the code value.

Parameters

sequence (DICOM keyword, no spaces or plural as per dictionary.) – DICOM sequence name.
dataset (DICOM dataset) – The DICOM dataset containing the sequence.

Returns

int. – code value

openrem.remapp.tools.get_values.get_value_kw(tag, dataset)¶

Get DICOM value by keyword reference.

Parameters

tag (str.) – DICOM keyword, no spaces or plural as per dictionary.
dataset (dataset) – The DICOM dataset containing the tag.

Returns

str. – value

openrem.remapp.tools.get_values.get_value_num(tag, dataset)¶

Get DICOM value by tag group and element number.

Always use get_value_kw by preference for readability. This module can be required when reading private elements.

Parameters

tag (hex) – DICOM group and element number as a single hexadecimal number (prefix 0x).
dataset (dataset) – The DICOM dataset containing the tag.

Returns

str. – value

openrem.remapp.tools.get_values.list_to_string(dicom_value)¶: Turn multivalue names into a single string for correct encoding and pretty reproduction :param dicom_value: returned DICOM value, usually a name field. Might be single (string) or multivalue (list) :return: string of name(s)

openrem.remapp.tools.get_values.return_for_export(model, field)¶: Prevent errors due to missing data in models :param model: database table :param field: database field :return: value or None

openrem.remapp.tools.get_values.test_numeric_value(string_number)¶: Tests if string can be converted to a float. If it can, return it :param string_number: string to test if is a number :return: string if number, nothing otherwise

openrem.remapp.tools.get_values.to_decimal_value(string_number)¶: Tests if string can be converted to a float. If yes returns it as decimal. :param string_number: string to test if a number :return: Decimal if convertable, None otherwise

Check if UID exists¶

Small module to check if UID already exists in the database.

openrem.remapp.tools.check_uid.check_uid(uid, level='Study')¶

Check if UID already exists in database.

Parameters: uid (str.) – Study UID.
Returns: 1 if it does exist, 0 otherwise

openrem.remapp.tools.check_uid.record_sop_instance_uid(study, sop_instance_uid)¶

Record the object’s SOP Instance UID so we can ignore it next time. If an object does need to be imported again, the original one needs to be deleted first.

Parameters

study – GeneralStudyModuleAttr database object
sop_instance_uid – SOP Instance UID of object being imported

Returns

DICOM time and date values¶

Module to convert betweeen DICOM and Python dates and times.

openrem.remapp.tools.dcmdatetime.get_date(tag, dataset)¶

Get DICOM date string and return Python date.

Parameters

tag (str.) – DICOM keyword, no spaces or plural as per dictionary.
dataset (dataset) – The DICOM dataset containing the tag.

Returns

Python date value

openrem.remapp.tools.dcmdatetime.get_date_time(tag, dataset)¶

Get DICOM date time string and return Python date time.

Parameters

tag (str.) – DICOM keyword, no spaces or plural as per dictionary.
dataset (dataset) – The DICOM dataset containing the tag.

Returns

Python date time value

openrem.remapp.tools.dcmdatetime.get_time(tag, dataset)¶

Get DICOM time string and return Python time.

Parameters

tag (str.) – DICOM keyword, no spaces or plural as per dictionary.
dataset (dataset) – The DICOM dataset containing the tag.

Returns

python time value

openrem.remapp.tools.dcmdatetime.make_date(dicomdate)¶

Given a DICOM date, return a Python date.

Parameters: dicomdate (str.) – DICOM style date.
Returns: Python date value

openrem.remapp.tools.dcmdatetime.make_date_time(dicomdatetime)¶

Given a DICOM date time, return a Python date time.

Parameters: dicomdate (str.) – DICOM style date time.
Returns: Python date time value

openrem.remapp.tools.dcmdatetime.make_dcm_date(pythondate)¶: Given a Python date, return a DICOM date :param pythondate: Date :type pythondate: Python date object :returns: DICOM date as string

openrem.remapp.tools.dcmdatetime.make_dcm_date_range(date1=None, date2=None, single_date=False)¶

Given one or two dates of the form yyyy-mm-dd, return a DICOM date range.

Parameters

date1 – Date from, string, yyyy-mm-dd, 1900-01-01 if None or badly formatted
date2 – Date until, string, yyyy-mm-dd, today if None or badly formatted
single_date – Single date range, bool, default False

Returns

DICOM formatted date range or single date

openrem.remapp.tools.dcmdatetime.make_dcm_time(python_time)¶

Return DICOM formatted time without seconds from python time

Parameters: python_time – Python datetime.time object
Returns: string, %H%M

openrem.remapp.tools.dcmdatetime.make_dcm_time_range(time1=None, time2=None)¶

Given one or two times of the format 0123, return DICOM formatted time range (without seconds)

Parameters

time1 – time, format 0123, 0000 if None
time2 – time, format 0123, 2359 if None

Returns

time range, string, format 0123-1234

openrem.remapp.tools.dcmdatetime.make_time(dicomtime)¶

Given a DICOM time, return a Python time.

Parameters: dicomdate (str.) – DICOM style time.
Returns: Python time value

Test for QA or other non-patient related studies¶

openrem.remapp.tools.not_patient_indicators.get_not_pt(dataset)¶

Looks for indications that a study might be a test or QA study.

Some values that might indicate a study was for QA or similar purposes are not recorded in the database, for example patient name. Therefore this module attempts to find such indications and creates an xml style string that can be recorded in the database on study import.

Parameters: dataset (dataset) – The DICOM dataset.
Returns: str. – xml style string if any trigger values are found.

Models¶

class remapp.models.AccumCassetteBsdProjRadiogDose(*args, **kwargs)¶

Accumulated Cassette-based Projection Radiography Dose TID 10006

From DICOM Part 16 Correction Proposal CP-1077:: This template provides information on Projection Radiography dose values accumulated on Cassette- based systems over one or more irradiation events (typically a study or a performed procedure step) from the same equipment.

exception DoesNotExist¶

exception MultipleObjectsReturned¶

class remapp.models.AccumIntegratedProjRadiogDose(*args, **kwargs)¶

Accumulated Integrated Projection Radiography Dose TID 10007

From DICOM Part 16 Correction Proposal CP-1077:: This template provides information on Projection Radiography dose values accumulated on Integrated systems over one or more irradiation events (typically a study or a performed procedure step) from the same equipment.

exception DoesNotExist¶

exception MultipleObjectsReturned¶

convert_gym2_to_cgycm2()¶: Converts Gy.m2 to cGy.cm2 for display in web interface

total_dap_delta_gym2_to_cgycm2()¶: Converts total DAP over delta days from Gy.m2 to cGy.cm2 for display in web interface

class remapp.models.AccumMammographyXRayDose(*args, **kwargs)¶

Accumulated Mammography X-Ray Dose TID 10005

From DICOM Part 16:: This modality specific template provides detailed information on mammography X-Ray dose value accumulations over several irradiation events from the same equipment (typically a study or a performed procedure step).

exception DoesNotExist¶

exception MultipleObjectsReturned¶

class remapp.models.AccumProjXRayDose(*args, **kwargs)¶

Accumulated Fluoroscopy and Acquisition Projection X-Ray Dose TID 10004

From DICOM Part 16:: This general template provides detailed information on projection X-Ray dose value accumulations over several irradiation events from the same equipment (typically a study or a performed procedure step).

exception DoesNotExist¶

exception MultipleObjectsReturned¶

acq_gym2_to_cgycm2()¶: Converts acquisition DAP total from Gy.m2 to cGy.cm2 for display in web interface

fluoro_gym2_to_cgycm2()¶: Converts fluoroscopy DAP total from Gy.m2 to cGy.cm2 for display in web interface

class remapp.models.AccumXRayDose(*args, **kwargs)¶

Accumulated X-Ray Dose TID 10002

From DICOM Part 16:: This general template provides detailed information on projection X-Ray dose value accumulations over several irradiation events from the same equipment (typically a study or a performed procedure step).

exception DoesNotExist¶

exception MultipleObjectsReturned¶

class remapp.models.AdminTaskQuestions(*args, **kwargs)¶

Record if admin tasks have been dealt with

exception DoesNotExist¶

exception MultipleObjectsReturned¶

class remapp.models.BackgroundTask(id, uuid, proc_id, task_type, info, error, completed_successfully, complete, started_at)¶

exception DoesNotExist¶

exception MultipleObjectsReturned¶

class remapp.models.BackgroundTaskMaximumRows(*args, **kwargs)¶

Table to store the maximum number of rows allowed in the BackgroundTask table

exception DoesNotExist¶

exception MultipleObjectsReturned¶

class remapp.models.BillingCode(id, radiopharmaceutical_administration_event_data, billing_code)¶

exception DoesNotExist¶

exception MultipleObjectsReturned¶

class remapp.models.Calibration(*args, **kwargs)¶

Table to hold the calibration information

Container in TID 10002 Accumulated X-ray dose

exception DoesNotExist¶

exception MultipleObjectsReturned¶

class remapp.models.ContextID(*args, **kwargs)¶

Table to hold all the context ID code values and code meanings.

Could be prefilled from the tables in DICOM 3.16, but is actually populated as the codes occur. This assumes they are used correctly.

exception DoesNotExist¶

exception MultipleObjectsReturned¶

class remapp.models.CtAccumulatedDoseData(*args, **kwargs)¶

CT Accumulated Dose Data

From DICOM Part 16:: This general template provides detailed information on CT X-Ray dose value accumulations over several irradiation events from the same equipment and over the scope of accumulation specified for the report (typically a Study or a Performed Procedure Step).

exception DoesNotExist¶

exception MultipleObjectsReturned¶

class remapp.models.CtDoseCheckDetails(*args, **kwargs)¶

CT Dose Check Details TID 10015

From DICOM Part 16:: This template records details related to the use of the NEMA Dose Check Standard (NEMA XR-25-2010).

exception DoesNotExist¶

exception MultipleObjectsReturned¶

class remapp.models.CtIrradiationEventData(*args, **kwargs)¶

CT Irradiation Event Data TID 10013

From DICOM Part 16:

This template conveys the dose and equipment parameters of a single irradiation event.

Additional to the template:

date_time_started
series_description

exception DoesNotExist¶

exception MultipleObjectsReturned¶

class remapp.models.CtRadiationDose(*args, **kwargs)¶

CT Radiation Dose TID 10011

From DICOM Part 16:: This template defines a container (the root) with subsidiary content items, each of which corresponds to a single CT X-Ray irradiation event entry. There is a defined recording observer (the system or person responsible for recording the log, generally the system). Accumulated values shall be kept for a whole Study or at least a part of a Study, if the Study is divided in the workflow of the examination, or a performed procedure step. Multiple CT Radiation Dose objects may be created for one Study.

exception DoesNotExist¶

exception MultipleObjectsReturned¶

class remapp.models.CtReconstructionAlgorithm(*args, **kwargs)¶

Container in TID 10013 to hold CT reconstruction methods

exception DoesNotExist¶

exception MultipleObjectsReturned¶

class remapp.models.CtXRaySourceParameters(*args, **kwargs)¶

Container in TID 10013 to hold CT x-ray source parameters

exception DoesNotExist¶

exception MultipleObjectsReturned¶

class remapp.models.DeviceParticipant(*args, **kwargs)¶

Device Participant TID 1021

From DICOM Part 16:: This template describes a device participating in an activity as other than an observer or subject. E.g. for a dose report documenting an irradiating procedure, participants include the irradiating device.

exception DoesNotExist¶

exception MultipleObjectsReturned¶

class remapp.models.DicomDeleteSettings(*args, **kwargs)¶

Table to store DICOM deletion settings

exception DoesNotExist¶

exception MultipleObjectsReturned¶

class remapp.models.DicomQRRspImage(id, dicom_qr_rsp_series, query_id, sop_instance_uid, instance_number, sop_class_uid, deleted_flag, deleted_reason)¶

exception DoesNotExist¶

exception MultipleObjectsReturned¶

class remapp.models.DicomQRRspSeries(id, dicom_qr_rsp_study, query_id, series_instance_uid, series_number, series_time, modality, series_description, number_of_series_related_instances, station_name, sop_class_in_series, image_level_move, deleted_flag, deleted_reason)¶

exception DoesNotExist¶

exception MultipleObjectsReturned¶

class remapp.models.DicomQRRspStudy(id, dicom_query, query_id, study_instance_uid, modality, modalities_in_study, study_description, number_of_study_related_series, sop_classes_in_study, station_name, deleted_flag, deleted_reason)¶

exception DoesNotExist¶

exception MultipleObjectsReturned¶

class remapp.models.DicomQuery(*args, **kwargs)¶

Table to store DICOM query settings

exception DoesNotExist¶

exception MultipleObjectsReturned¶

class remapp.models.DicomRemoteQR(*args, **kwargs)¶

Table to store DICOM remote QR settings

exception DoesNotExist¶

exception MultipleObjectsReturned¶

class remapp.models.DicomStoreSCP(*args, **kwargs)¶

Table to store DICOM store settings

exception DoesNotExist¶

exception MultipleObjectsReturned¶

class remapp.models.DoseRelatedDistanceMeasurements(*args, **kwargs)¶

Dose Related Distance Measurements Context ID 10008

Called from TID 10003c

exception DoesNotExist¶

exception MultipleObjectsReturned¶

class remapp.models.DrugProductIdentifier(id, radiopharmaceutical_administration_event_data, drug_product_identifier)¶

exception DoesNotExist¶

exception MultipleObjectsReturned¶

class remapp.models.Exports(*args, **kwargs)¶

Table to hold the export status and filenames

exception DoesNotExist¶

exception MultipleObjectsReturned¶

class remapp.models.Exposure(*args, **kwargs)¶

In TID 10003b. Code value 113736 (uA.s)

exception DoesNotExist¶

exception MultipleObjectsReturned¶

convert_uAs_to_mAs()¶: Converts uAs to mAs for display in web interface

class remapp.models.GeneralEquipmentModuleAttr(*args, **kwargs)¶

General Equipment Module C.7.5.1

From DICOM Part 3: Information Object Definitions Table C.7-8:: Specifies the Attributes that identify and describe the piece of equipment that produced a Series of Composite Instances.

exception DoesNotExist¶

exception MultipleObjectsReturned¶

class remapp.models.GeneralStudyModuleAttr(*args, **kwargs)¶

General Study Module C.7.2.1

Specifies the Attributes that describe and identify the Study performed upon the Patient. From DICOM Part 3: Information Object Definitions Table C.7-3

Additional to the module definition:

performing_physician_name
operator_name
modality_type
procedure_code_value_and_meaning
requested_procedure_code_value_and_meaning

exception DoesNotExist¶

exception MultipleObjectsReturned¶

dap_a_cgycm2()¶: Converts DAP A to cGy.cm2 from Gy.m2 for display or export

dap_b_cgycm2()¶: Converts DAP B to cGy.cm2 from Gy.m2 for display or export

dap_delta_weeks_cgycm2()¶: Converts DAP delta weeks to cGy.cm2 from Gy.m2 for display

dap_total_cgycm2()¶: Converts DAP A+B to cGy.cm2 from Gy.m2 for display or export

class remapp.models.GlomerularFiltrationRate(id, radiopharmaceutical_administration_patient_characteristics, glomerular_filtration_rate, measurement_method, equivalent_meaning_of_concept_name)¶

exception DoesNotExist¶

exception MultipleObjectsReturned¶

class remapp.models.HighDoseMetricAlertRecipients(*args, **kwargs)¶

Table to store whether users should receive high dose fluoroscopy alerts

exception DoesNotExist¶

exception MultipleObjectsReturned¶

class remapp.models.HighDoseMetricAlertSettings(*args, **kwargs)¶

Table to store high dose fluoroscopy alert settings

exception DoesNotExist¶

exception MultipleObjectsReturned¶

class remapp.models.HomePageAdminSettings(*args, **kwargs)¶

Table to store home page settings

exception DoesNotExist¶

exception MultipleObjectsReturned¶

class remapp.models.ImageViewModifier(*args, **kwargs)¶

Table to hold image view modifiers for the irradiation event x-ray data table

From DICOM Part 16 Annex D DICOM controlled Terminology Definitions

Code Value 111032
Code Meaning Image View Modifier
Code Definition Modifier for image view

exception DoesNotExist¶

exception MultipleObjectsReturned¶

class remapp.models.IntravenousExtravasationSymptoms(id, radiopharmaceutical_administration_event_data, intravenous_extravasation_symptoms)¶

exception DoesNotExist¶

exception MultipleObjectsReturned¶

class remapp.models.IrradEventXRayData(*args, **kwargs)¶

Irradiation Event X-Ray Data TID 10003

From DICOM part 16:: This template conveys the dose and equipment parameters of a single irradiation event.

exception DoesNotExist¶

exception MultipleObjectsReturned¶

class remapp.models.IrradEventXRayDetectorData(*args, **kwargs)¶

Irradiation Event X-Ray Detector Data TID 10003a

From DICOM Part 16 Correction Proposal CP-1077:: This template contains data which is expected to be available to the X-ray detector or plate reader component of the equipment.

exception DoesNotExist¶

exception MultipleObjectsReturned¶

class remapp.models.IrradEventXRayMechanicalData(*args, **kwargs)¶

Irradiation Event X-Ray Mechanical Data TID 10003c

From DICOM Part 16 Correction Proposal CP-1077:

This template contains data which is expected to be available to the gantry or mechanical component of the equipment.

Additional to the template:

compression_force
magnification_factor

exception DoesNotExist¶

exception MultipleObjectsReturned¶

class remapp.models.IrradEventXRaySourceData(*args, **kwargs)¶

Irradiation Event X-Ray Source Data TID 10003b

From DICOM Part 16 Correction Proposal CP-1077:

This template contains data which is expected to be available to the X-ray source component of the equipment.

Additional to the template:

ii_field_size
exposure_control_mode
grid information over and above grid type

exception DoesNotExist¶

exception MultipleObjectsReturned¶

convert_gy_to_mgy()¶: Converts Gy to mGy for display in web interface

class remapp.models.Kvp(*args, **kwargs)¶

In TID 10003b. Code value 113733 (kV)

exception DoesNotExist¶

exception MultipleObjectsReturned¶

class remapp.models.LanguageofContentItemandDescendants(id, radiopharmaceutical_radiation_dose, language_of_contentitem_and_descendants, country_of_language)¶

exception DoesNotExist¶

exception MultipleObjectsReturned¶

class remapp.models.MergeOnDeviceObserverUIDSettings(*args, **kwargs)¶

Table to store setting(s) for autmoatic setting of Display Name and Modality type based on same Device observer UID

exception DoesNotExist¶

exception MultipleObjectsReturned¶

class remapp.models.NotPatientIndicatorsID(*args, **kwargs)¶

Table to record strings that indicate a patient ID is really a test or QA ID

exception DoesNotExist¶

exception MultipleObjectsReturned¶

class remapp.models.NotPatientIndicatorsName(*args, **kwargs)¶

Table to record strings that indicate a patient name is really a test or QA name

exception DoesNotExist¶

exception MultipleObjectsReturned¶

class remapp.models.ObjectUIDsProcessed(*args, **kwargs)¶

Table to hold the SOP Instance UIDs of the objects that have been processed against this study to enable duplicate sorting.

exception DoesNotExist¶

exception MultipleObjectsReturned¶

class remapp.models.ObserverContext(*args, **kwargs)¶

Observer Context TID 1002

From DICOM Part 16:: The observer (person or device) that created the Content Items to which this context applies.

exception DoesNotExist¶

exception MultipleObjectsReturned¶

class remapp.models.OpenSkinSafeList(*args, **kwargs)¶

Table to store systems names and software versions that are suitable for OpenSkin

exception DoesNotExist¶

exception MultipleObjectsReturned¶

class remapp.models.OrganDose(id, radiopharmaceutical_administration_event_data, finding_site, laterality, mass, measurement_method, organ_dose, reference_authority_code, reference_authority_text, type_of_detector_motion)¶

exception DoesNotExist¶

exception MultipleObjectsReturned¶

class remapp.models.PETSeries(id, radiopharmaceutical_radiation_dose, series_uid, series_datetime, number_of_rr_intervals, number_of_time_slots, number_of_time_slices, number_of_slices, reconstruction_method, coincidence_window_width, energy_window_lower_limit, energy_window_upper_limit, scan_progression_direction)¶

exception DoesNotExist¶

exception MultipleObjectsReturned¶

class remapp.models.PETSeriesCorrection(id, pet_series, corrected_image)¶

exception DoesNotExist¶

exception MultipleObjectsReturned¶

class remapp.models.PETSeriesType(id, pet_series, series_type)¶

exception DoesNotExist¶

exception MultipleObjectsReturned¶

class remapp.models.PKsForSummedRFDoseStudiesInDeltaWeeks(*args, **kwargs)¶

Table to hold foreign keys of all studies that fall within the delta weeks of each RF study.

exception DoesNotExist¶

exception MultipleObjectsReturned¶

class remapp.models.PatientIDSettings(*args, **kwargs)¶

Table to store patient ID settings

exception DoesNotExist¶

exception MultipleObjectsReturned¶

class remapp.models.PatientModuleAttr(*args, **kwargs)¶

Patient Module C.7.1.1

From DICOM Part 3: Information Object Definitions Table C.7-1:: Specifies the Attributes of the Patient that describe and identify the Patient who is the subject of a diagnostic Study. This Module contains Attributes of the patient that are needed for diagnostic interpretation of the Image and are common for all studies performed on the patient. It contains Attributes that are also included in the Patient Modules in Section C.2.

exception DoesNotExist¶

exception MultipleObjectsReturned¶

class remapp.models.PatientState(id, radiopharmaceutical_administration_patient_characteristics, patient_state)¶

exception DoesNotExist¶

exception MultipleObjectsReturned¶

class remapp.models.PatientStudyModuleAttr(*args, **kwargs)¶

Patient Study Module C.7.2.2

From DICOM Part 3: Information Object Definitions Table C.7-4a:: Defines Attributes that provide information about the Patient at the time the Study started.

exception DoesNotExist¶

exception MultipleObjectsReturned¶

class remapp.models.PersonParticipant(*args, **kwargs)¶

Person Participant TID 1020

From DICOM Part 16:: This template describes a person participating in an activity as other than an observer or subject. E.g. for a dose report documenting an irradiating procedure, participants include the person administering the irradiation and the person authorizing the irradiation.

exception DoesNotExist¶

exception MultipleObjectsReturned¶

class remapp.models.ProjectionXRayRadiationDose(*args, **kwargs)¶

Projection X-Ray Radiation Dose template TID 10001

From DICOM Part 16:: This template defines a container (the root) with subsidiary content items, each of which represents a single projection X-Ray irradiation event entry or plane-specific dose accumulations. There is a defined recording observer (the system or person responsible for recording the log, generally the system). A Biplane irradiation event will be recorded as two individual events, one for each plane. Accumulated values will be kept separate for each plane.

exception DoesNotExist¶

exception MultipleObjectsReturned¶

class remapp.models.PulseWidth(*args, **kwargs)¶

In TID 10003b. Code value 113793 (ms)

exception DoesNotExist¶

exception MultipleObjectsReturned¶

class remapp.models.RadionuclideIdentifier(id, radiopharmaceutical_administration_event_data, radionuclide_identifier)¶

exception DoesNotExist¶

exception MultipleObjectsReturned¶

class remapp.models.RadiopharmaceuticalAdministrationEventData(id, radiopharmaceutical_radiation_dose, radiopharmaceutical_agent, radiopharmaceutical_agent_string, radionuclide, radionuclide_half_life, radiopharmaceutical_specific_activity, radiopharmaceutical_administration_event_uid, estimated_extravasation_activity, radiopharmaceutical_start_datetime, radiopharmaceutical_stop_datetime, administered_activity, effective_dose, radiopharmaceutical_volume, pre_administration_measured_activity, pre_activity_measurement_device, post_administration_measured_activity, post_activity_measurement_device, route_of_administration, site_of, laterality, brand_name, radiopharmaceutical_dispense_unit_identifier, prescription_identifier, comment)¶

exception DoesNotExist¶

exception MultipleObjectsReturned¶

class remapp.models.RadiopharmaceuticalAdministrationPatientCharacteristics(id, radiopharmaceutical_radiation_dose, subject_age, subject_sex, patient_height, patient_weight, body_surface_area, body_surface_area_formula, body_mass_index, equation, glucose, fasting_duration, hydration_volume, recent_physical_activity, serum_creatinine)¶

exception DoesNotExist¶

exception MultipleObjectsReturned¶

class remapp.models.RadiopharmaceuticalLotIdentifier(id, radiopharmaceutical_administration_event_data, radiopharmaceutical_lot_identifier)¶

exception DoesNotExist¶

exception MultipleObjectsReturned¶

class remapp.models.RadiopharmaceuticalRadiationDose(*args, **kwargs)¶

Radiopharmaceutical Radiation Dose TID 10021

From DICOM Part 16:: This Template defines a container (the root) with subsidiary Content Items, each of which corresponds to a single Radiopharmaceutical Administration Dose event entry. There is a defined recording observer (the system and/or person responsible for recording the assay of the radiopharmaceutical, and the person administered the radiopharmaceutical). Multiple Radiopharmaceutical Radiation Dose objects may be created for one study. Radiopharmaceutical Start DateTime in TID 10022 “Radiopharmaceutical Administration Event Data” will convey the order of administrations.

exception DoesNotExist¶

exception MultipleObjectsReturned¶

class remapp.models.ReagentVialIdentifier(id, radiopharmaceutical_administration_event_data, reagent_vial_identifier)¶

exception DoesNotExist¶

exception MultipleObjectsReturned¶

class remapp.models.ScanningLength(*args, **kwargs)¶

Scanning Length TID 10014

From DICOM Part 16:: No description

exception DoesNotExist¶

exception MultipleObjectsReturned¶

class remapp.models.SizeSpecificDoseEstimation(*args, **kwargs)¶

Container in TID 10013 to hold size specific dose estimation details

exception DoesNotExist¶

exception MultipleObjectsReturned¶

class remapp.models.SizeUpload(*args, **kwargs)¶

Table to store patient size information

exception DoesNotExist¶

exception MultipleObjectsReturned¶

class remapp.models.SkinDoseMapCalcSettings(*args, **kwargs)¶

Table to store skin dose map calculation settings

exception DoesNotExist¶

exception MultipleObjectsReturned¶

class remapp.models.SkinDoseMapResults(*args, **kwargs)¶

Table to hold the results from OpenSkin

exception DoesNotExist¶

exception MultipleObjectsReturned¶

class remapp.models.SourceOfCTDoseInformation(*args, **kwargs)¶

Source of CT Dose Information

exception DoesNotExist¶

exception MultipleObjectsReturned¶

class remapp.models.StandardNameSettings(*args, **kwargs)¶

Table to store standard name mapping settings

exception DoesNotExist¶

exception MultipleObjectsReturned¶

class remapp.models.StandardNames(*args, **kwargs)¶

Table to store standard study description, requested procedure, procedure or acquisition names

exception DoesNotExist¶

exception MultipleObjectsReturned¶

class remapp.models.SummaryFields(*args, **kwargs)¶

Status and progress of populating the summary fields in GeneralStudyModuleAttr

exception DoesNotExist¶

exception MultipleObjectsReturned¶

class remapp.models.UniqueEquipmentNames(*args, **kwargs)¶

Table to unique equipment name information

exception DoesNotExist¶

exception MultipleObjectsReturned¶

class remapp.models.UpgradeStatus(*args, **kwargs)¶

Record upgrade status activity

exception DoesNotExist¶

exception MultipleObjectsReturned¶

class remapp.models.UserProfile(*args, **kwargs)¶

Table to store user profile settings

exception DoesNotExist¶

exception MultipleObjectsReturned¶

class remapp.models.WEDSeriesOrInstances(*args, **kwargs)¶

From TID 10013 Series or Instance used for Water Equivalent Diameter estimation

exception DoesNotExist¶

exception MultipleObjectsReturned¶

class remapp.models.XrayFilters(*args, **kwargs)¶

Container in TID 10003b. Code value 113771

exception DoesNotExist¶

exception MultipleObjectsReturned¶

class remapp.models.XrayGrid(*args, **kwargs)¶

Content ID 10017 X-Ray Grid

From DICOM Part 16

exception DoesNotExist¶

exception MultipleObjectsReturned¶

class remapp.models.XrayTubeCurrent(*args, **kwargs)¶

In TID 10003b. Code value 113734 (mA)

exception DoesNotExist¶

exception MultipleObjectsReturned¶

remapp.models.create_or_save_high_dose_metric_alert_recipient_setting(sender, instance, **kwargs)¶: Function to create or save fluoroscopy high dose alert recipient settings

remapp.models.limit_background_task_table_rows(sender, instance, **kwargs)¶: Method to limit the number of rows in the BackgroundTask table. This method is triggered by a post_save signal associated with the BackgroundTask table.

Filtering code¶

class remapp.interface.mod_filters.CTFilterPlusPid(*args, **kwargs)¶: Adding patient name and ID to filter if permissions allow

class remapp.interface.mod_filters.CTFilterPlusPidPlusStdNames(*args, **kwargs)¶: Adding standard name fields

class remapp.interface.mod_filters.CTFilterPlusStdNames(data=None, queryset=None, *, request=None, prefix=None)¶: Adding standard name fields

class remapp.interface.mod_filters.CTSummaryListFilter(data=None, queryset=None, *, request=None, prefix=None)¶

Filter for CT studies to display in web interface.

class Meta¶

Lists fields and order-by information for django-filter filtering

model¶: alias of GeneralStudyModuleAttr

class remapp.interface.mod_filters.DXFilterPlusPid(*args, **kwargs)¶: Adding patient name and ID to filter if permissions allow

class remapp.interface.mod_filters.DXFilterPlusPidPlusStdNames(*args, **kwargs)¶: Adding standard name fields

class remapp.interface.mod_filters.DXFilterPlusStdNames(data=None, queryset=None, *, request=None, prefix=None)¶: Adding standard name fields

class remapp.interface.mod_filters.DXSummaryListFilter(data=None, queryset=None, *, request=None, prefix=None)¶

Filter for DX studies to display in web interface.

class Meta¶

Lists fields and order-by information for django-filter filtering

model¶: alias of GeneralStudyModuleAttr

class remapp.interface.mod_filters.DateTimeOrderingFilter(*args, **kwargs)¶

Custom filter to order by date and time as they are two seperate fields

filter(qs, value)¶

Sets order_by to date then time and returns queryset

Parameters

qs – queryset
value – list containing ordering type as string

Returns

ordered queryset

class remapp.interface.mod_filters.MGFilterPlusPid(*args, **kwargs)¶: Adding patient name and ID to filter if permissions allow

class remapp.interface.mod_filters.MGFilterPlusPidPlusStdNames(*args, **kwargs)¶: Adding standard name fields

class remapp.interface.mod_filters.MGFilterPlusStdNames(data=None, queryset=None, *, request=None, prefix=None)¶: Adding standard name fields

class remapp.interface.mod_filters.MGSummaryListFilter(data=None, queryset=None, *, request=None, prefix=None)¶

Filter for mammography studies to display in web interface.

class Meta¶

Lists fields and order-by information for django-filter filtering

model¶: alias of GeneralStudyModuleAttr

class remapp.interface.mod_filters.NMFilterPlusPid(*args, **kwargs)¶: Adding patient name and ID to filter if permissions allow

class remapp.interface.mod_filters.NMSummaryListFilter(data=None, queryset=None, *, request=None, prefix=None)¶

Filter for NM studies to display in web interface.

class Meta¶

Lists fields and order-by information for django-filter filtering

model¶: alias of GeneralStudyModuleAttr

class remapp.interface.mod_filters.RFFilterPlusPid(*args, **kwargs)¶: Adding patient name and ID to filter if permissions allow

class remapp.interface.mod_filters.RFFilterPlusPidPlusStdNames(*args, **kwargs)¶: Adding standard name fields

class remapp.interface.mod_filters.RFFilterPlusStdNames(data=None, queryset=None, *, request=None, prefix=None)¶: Adding standard name fields

class remapp.interface.mod_filters.RFSummaryListFilter(data=None, queryset=None, *, request=None, prefix=None)¶

Filter for fluoroscopy studies to display in web interface.

class Meta¶

Lists fields and order-by information for django-filter filtering

model¶: alias of GeneralStudyModuleAttr

remapp.interface.mod_filters.custom_id_filter(queryset, name, value)¶: Search for ID as plain text and encrypted

remapp.interface.mod_filters.custom_name_filter(queryset, name, value)¶: Search for name as plain text and encrypted

Views¶

remapp.views.ct_detail_view(request, pk=None)¶: Detail view for a CT study.

remapp.views.ct_summary_list_filter(request)¶: Obtain data for CT summary view.

remapp.views.dx_detail_view(request, pk=None)¶: Detail view for a DX study.

remapp.views.dx_summary_list_filter(request)¶: Obtain data for radiographic summary view.

remapp.views.logout_page(request)¶: Log users out and re-direct them to the main page.

remapp.views.mg_detail_view(request, pk=None)¶: Detail view for a CT study.

remapp.views.mg_summary_list_filter(request)¶: Mammography data for summary view.

remapp.views.multiply(value, arg)¶

Return multiplication within Django templates

Parameters

value – the value to multiply
arg – the second value to multiply

Returns

the multiplication

remapp.views.nm_detail_view(request, pk=None)¶: Detail view for a NM study.

remapp.views.nm_summary_list_filter(request)¶: Obtain data for NM summary view.

remapp.views.rf_detail_view(request, pk=None)¶: Detail view for an RF study.

remapp.views.rf_detail_view_skin_map(request, pk=None)¶: View to calculate a skin dose map. Currently just a copy of rf_detail_view.

remapp.views.rf_summary_list_filter(request)¶: Obtain data for radiographic summary view.

remapp.views.standard_name_settings()¶: Obtain the system-level enable_standard_names setting.

remapp.views.update_latest_studies(request)¶

AJAX function to calculate the latest studies for each display name for a particular modality.

Parameters: request – Request object
Returns: HTML table of modalities

openSkin related views¶

openSkin related views.

class remapp.views_openskin.SkinDoseMapCalcSettingsUpdate(**kwargs)¶

Update skin dose map calculation settings.

form_class¶: alias of SkinDoseMapCalcSettingsForm

form_valid(form)¶: If the form is valid, save the associated model.

get_context_data(**context)¶: Insert the form into the context dict.

model¶: alias of SkinDoseMapCalcSettings

class remapp.views_openskin.SkinSafeListCreate(**kwargs)¶

Enable skin map calculations by adding model, or model and software version to OpenSkinSafeList.

form_class¶: alias of SkinSafeListForm

form_valid(form)¶: If the form is valid, save the associated model.

get_context_data(**context)¶: Insert the form into the context dict.

model¶: alias of OpenSkinSafeList

class remapp.views_openskin.SkinSafeListDelete(**kwargs)¶

Disable skin map calculations for particular model or model and software version.

delete(request, *args, **kwargs)¶: Call the delete() method on the fetched object and then redirect to the success URL.

get_context_data(**context)¶: Insert the single object into the context dict.

model¶: alias of OpenSkinSafeList

class remapp.views_openskin.SkinSafeListUpdate(**kwargs)¶

Add or remove the software version restriction.

form_class¶: alias of SkinSafeListForm

form_valid(form)¶: If the form is valid, save the associated model.

get_context_data(**context)¶: Insert the form into the context dict.

model¶: alias of OpenSkinSafeList

remapp.views_openskin.check_skin_safe_model(skin_safe_models)¶

Check if device matches on manufacturer and model without version restriction.

openSkin safe list OpenSkinSafeList is checked against manufacturer and model. This function is then used to check if there are any entries on the list where software_version is blank.

Parameters

skin_safe_models (OpenSkinSafeList queryset) – Queryset of safe list entries matching manufacturer and model

Returns

safe_list_model_pk (int or None) – Primary key of entry if match found, None otherwise
model_enabled (bool) – True if match found with blank software_version, otherwise False

remapp.views_openskin.display_name_skin_enabled(request)¶: AJAX view to display if skin map calculations are enabled and links to change the configuration.

remapp.views_openskin.get_matching_equipment_names(manufacturer, model_name)¶

Get queryset of unique equipment names that match the manufacturer and model name being reviewed.

Filters the UniqueEquipmentNames table for fluoroscopy entries (or dual fluoro + radiography) that match the manufacturer and model that has been selected.

Parameters

manufacturer (str) – Name of manufacturer from UniqueEquipmentNames table
model_name (str) – Model name from UniqueEquipmentNames table

Returns

Queryset filtered for fluoro systems matching the manufacturer and model name

Return type

UniqueEquipmentNames queryset

Export Views¶

remapp.exports.exportviews.ct_xlsx_phe2019(request)¶

View to launch task to export CT studies to xlsx file in PHE 2019 CT survey format

Parameters: request – Contains the database filtering parameters and user details.

remapp.exports.exportviews.ctcsv1(request, name=None, pat_id=None)¶

View to launch task to export CT studies to csv file

Parameters

request (GET) – Contains the database filtering parameters. Also used to get user group.
name – string, 0 or 1 from URL indicating if names should be exported
pat_id – string, 0 or 1 from URL indicating if patient ID should be exported

remapp.exports.exportviews.ctxlsx1(request, name=None, pat_id=None)¶

View to launch task to export CT studies to xlsx file

Parameters

request (GET) – Contains the database filtering parameters. Also used to get user group.
name – string, 0 or 1 from URL indicating if names should be exported
pat_id – string, 0 or 1 from URL indicating if patient ID should be exported

remapp.exports.exportviews.deletefile(request)¶

View to delete export files from the server

Parameters: request (POST) – Contains the task ID

remapp.exports.exportviews.download(request, task_id)¶

View to handle downloads of files from the server

Originally used for download of the export spreadsheets, now also used for downloading the patient size import logfiles.

Parameters

request – Used to get user group.
task_id – ID of the export or logfile

remapp.exports.exportviews.dx_xlsx_phe2019(request, export_type=None)¶

View to launch task to export DX studies to xlsx file in PHE 2019 DX survey format

Parameters

request – Contains the database filtering parameters and user details.
export_type – string, ‘projection’ or ‘exam’

remapp.exports.exportviews.dxcsv1(request, name=None, pat_id=None)¶

View to launch task to export DX and CR studies to csv file

Parameters

request (GET) – Contains the database filtering parameters. Also used to get user group.
name – string, 0 or 1 from URL indicating if names should be exported
pat_id – string, 0 or 1 from URL indicating if patient ID should be exported

remapp.exports.exportviews.dxxlsx1(request, name=None, pat_id=None)¶

View to launch task to export DX and CR studies to xlsx file

Parameters

request (GET) – Contains the database filtering parameters. Also used to get user group.
name – string, 0 or 1 from URL indicating if names should be exported
pat_id – string, 0 or 1 from URL indicating if patient ID should be exported

remapp.exports.exportviews.export(request)¶

View to list current and completed exports to track progress, download and delete

Parameters: request – Used to get user group.

remapp.exports.exportviews.export_abort(request, pk)¶

View to abort current export job

Parameters: request (POST) – Contains the task primary key

remapp.exports.exportviews.export_remove(request, task_id=None)¶

Function to remove export task from queue

Parameters

request (POST) – Contains the task primary key
task_id – UUID of task in question

remapp.exports.exportviews.flcsv1(request, name=None, pat_id=None)¶

View to launch task to export fluoroscopy studies to csv file

Parameters

request (GET) – Contains the database filtering parameters. Also used to get user group.
name – string, 0 or 1 from URL indicating if names should be exported
pat_id – string, 0 or 1 from URL indicating if patient ID should be exported

remapp.exports.exportviews.include_pid(request, name, pat_id)¶: Check if user is allowed to export PID, then check if they have asked to. :param request: request so we can determine the user and therefore groups :param name: string, 0 or 1 from URL indicating if names should be exported :param pat_id: string, 0 or 1 from URL indicating if patient ID should be exported :return: dict, with pidgroup, include_names and include_pat_id as bools

remapp.exports.exportviews.mgcsv1(request, name=None, pat_id=None)¶: Launches export of mammo data to CSV :param request: Contains the database filtering parameters. Also used to get user group. :param name: string, 0 or 1 from URL indicating if names should be exported :param pat_id: string, 0 or 1 from URL indicating if patient ID should be exported :return:

remapp.exports.exportviews.mgnhsbsp(request)¶

View to launch task to export mammography studies to csv file using a NHSBSP template

Parameters: request (GET) – Contains the database filtering parameters. Also used to get user group.

remapp.exports.exportviews.mgxlsx1(request, name=None, pat_id=None)¶: Launches export of mammo data to xlsx :param request: Contains the database filtering parameters. Also used to get user group. :param name: string, 0 or 1 from URL indicating if names should be exported :param pat_id: string, 0 or 1 from URL indicating if patient ID should be exported :return:

remapp.exports.exportviews.nmcsv1(request, name=None, pat_id=None)¶

View to launch task to export NM studies to csv file

Parameters

request (GET) – Contains the database filtering parameters. Also used to get user group.
name – string, 0 or 1 from URL indicating if names should be exported
pat_id – string, 0 or 1 from URL indicating if patient ID should be exported

remapp.exports.exportviews.nmxlsx1(request, name=None, pat_id=None)¶

View to launch celery task to export NM studies to excel file

Parameters

request (GET) – Contains the database filtering parameters. Also used to get user group.
name – string, 0 or 1 from URL indicating if names should be exported
pat_id – string, 0 or 1 from URL indicating if patient ID should be exported

remapp.exports.exportviews.rf_xlsx_phe2019(request)¶

View to launch task to export fluoro studies to xlsx file in PHE 2019 IR/fluoro survey format

Parameters: request – Contains the database filtering parameters and user details.

remapp.exports.exportviews.rfopenskin(request, pk)¶: Create csv export suitable for import to standalone openSkin :param request: request object :param pk: primary key of study in GeneralStudyModuleAttr table

remapp.exports.exportviews.rfxlsx1(request, name=None, pat_id=None)¶

View to launch task to export fluoroscopy studies to xlsx file

Parameters

request (GET) – Contains the database filtering parameters. Also used to get user group.
name – string, 0 or 1 from URL indicating if names should be exported
pat_id – string, 0 or 1 from URL indicating if patient ID should be exported

remapp.exports.exportviews.update_active(request)¶

AJAX function to return active exports

Parameters: request – Request object
Returns: HTML table of active exports

remapp.exports.exportviews.update_complete(request)¶

AJAX function to return recently completed exports

Parameters: request – Request object, including pk of latest complete export at initial page load
Returns: HTML table of completed exports

remapp.exports.exportviews.update_error(request)¶

AJAX function to return exports in error state

Parameters: request – Request object
Returns: HTML table of exports in error state

remapp.exports.exportviews.update_queue(request)¶

AJAX function to return queued exports

Parameters: request – Request object
Returns: HTML table of active exports

Forms¶

class remapp.forms.BackgroundTaskMaximumRowsForm(*args, **kwargs)¶

Form for configuring the maximum number of rows in the BackgroundTask table

property media¶: Return all media required to render the widgets on this form.

class remapp.forms.CTChartOptionsDisplayForm(data=None, files=None, auto_id='id_%s', prefix=None, initial=None, error_class=<class 'django.forms.utils.ErrorList'>, label_suffix=None, empty_permitted=False, field_order=None, use_required_attribute=None, renderer=None)¶

Form for CT chart display options

property media¶: Return all media required to render the widgets on this form.

class remapp.forms.CTChartOptionsDisplayFormIncStandard(data=None, files=None, auto_id='id_%s', prefix=None, initial=None, error_class=<class 'django.forms.utils.ErrorList'>, label_suffix=None, empty_permitted=False, field_order=None, use_required_attribute=None, renderer=None)¶

property media¶: Return all media required to render the widgets on this form.

class remapp.forms.CTChartOptionsForm(data=None, files=None, auto_id='id_%s', prefix=None, initial=None, error_class=<class 'django.forms.utils.ErrorList'>, label_suffix=None, empty_permitted=False, field_order=None, use_required_attribute=None, renderer=None)¶

Form for CT chart options

property media¶: Return all media required to render the widgets on this form.

class remapp.forms.CTChartOptionsFormIncStandard(data=None, files=None, auto_id='id_%s', prefix=None, initial=None, error_class=<class 'django.forms.utils.ErrorList'>, label_suffix=None, empty_permitted=False, field_order=None, use_required_attribute=None, renderer=None)¶

property media¶: Return all media required to render the widgets on this form.

class remapp.forms.DXChartOptionsDisplayForm(data=None, files=None, auto_id='id_%s', prefix=None, initial=None, error_class=<class 'django.forms.utils.ErrorList'>, label_suffix=None, empty_permitted=False, field_order=None, use_required_attribute=None, renderer=None)¶

Form for DX chart display options

property media¶: Return all media required to render the widgets on this form.

class remapp.forms.DXChartOptionsDisplayFormIncStandard(data=None, files=None, auto_id='id_%s', prefix=None, initial=None, error_class=<class 'django.forms.utils.ErrorList'>, label_suffix=None, empty_permitted=False, field_order=None, use_required_attribute=None, renderer=None)¶

property media¶: Return all media required to render the widgets on this form.

class remapp.forms.DXChartOptionsForm(data=None, files=None, auto_id='id_%s', prefix=None, initial=None, error_class=<class 'django.forms.utils.ErrorList'>, label_suffix=None, empty_permitted=False, field_order=None, use_required_attribute=None, renderer=None)¶

Form for DX chart options

property media¶: Return all media required to render the widgets on this form.

class remapp.forms.DXChartOptionsFormIncStandard(data=None, files=None, auto_id='id_%s', prefix=None, initial=None, error_class=<class 'django.forms.utils.ErrorList'>, label_suffix=None, empty_permitted=False, field_order=None, use_required_attribute=None, renderer=None)¶

property media¶: Return all media required to render the widgets on this form.

class remapp.forms.DicomDeleteSettingsForm(*args, **kwargs)¶

Form for configuring whether DICOM objects are stored or deleted once processed

property media¶: Return all media required to render the widgets on this form.

class remapp.forms.DicomQRForm(*args, **kwargs)¶

Form for configuring remote Query Retrieve nodes

property media¶: Return all media required to render the widgets on this form.

class remapp.forms.DicomQueryForm(*args, **kwargs)¶

Form for launching DICOM Query

clean()¶: Validate the form data to clear modality selections if sr_only is selected. :return: Form with modalities _or_ sr_only selected

class date¶

date(year, month, day) –> date object

ctime()¶: Return ctime() style string.

fromisocalendar()¶

int, int, int -> Construct a date from the ISO year, week number and weekday.

This is the inverse of the date.isocalendar() function

fromisoformat()¶: str -> Construct a date from the output of date.isoformat()

fromordinal()¶: int -> date corresponding to a proleptic Gregorian ordinal.

fromtimestamp()¶

Create a date from a POSIX timestamp.

The timestamp is a number, e.g. created via time.time(), that is interpreted as local time.

isocalendar()¶: Return a 3-tuple containing ISO year, week number, and weekday.

isoformat()¶: Return string in ISO 8601 format, YYYY-MM-DD.

isoweekday()¶: Return the day of the week represented by the date. Monday == 1 … Sunday == 7

replace()¶: Return date with new specified fields.

strftime()¶: format -> strftime() style string.

timetuple()¶: Return time tuple, compatible with time.localtime().

today()¶: Current date or datetime: same as self.__class__.fromtimestamp(time.time()).

toordinal()¶: Return proleptic Gregorian ordinal. January 1 of year 1 is day 1.

weekday()¶: Return the day of the week represented by the date. Monday == 0 … Sunday == 6

property media¶: Return all media required to render the widgets on this form.

class remapp.forms.DicomStoreForm(*args, **kwargs)¶

Form for configuring local Store nodes

property media¶: Return all media required to render the widgets on this form.

class remapp.forms.GeneralChartOptionsDisplayForm(data=None, files=None, auto_id='id_%s', prefix=None, initial=None, error_class=<class 'django.forms.utils.ErrorList'>, label_suffix=None, empty_permitted=False, field_order=None, use_required_attribute=None, renderer=None)¶

Form for general chart display options

property media¶: Return all media required to render the widgets on this form.

class remapp.forms.HomepageOptionsForm(data=None, files=None, auto_id='id_%s', prefix=None, initial=None, error_class=<class 'django.forms.utils.ErrorList'>, label_suffix=None, empty_permitted=False, field_order=None, use_required_attribute=None, renderer=None)¶

Form for displaying and changing the home page options

property media¶: Return all media required to render the widgets on this form.

class remapp.forms.MGChartOptionsDisplayForm(data=None, files=None, auto_id='id_%s', prefix=None, initial=None, error_class=<class 'django.forms.utils.ErrorList'>, label_suffix=None, empty_permitted=False, field_order=None, use_required_attribute=None, renderer=None)¶

Form for MG chart display options

property media¶: Return all media required to render the widgets on this form.

class remapp.forms.MGChartOptionsDisplayFormIncStandard(data=None, files=None, auto_id='id_%s', prefix=None, initial=None, error_class=<class 'django.forms.utils.ErrorList'>, label_suffix=None, empty_permitted=False, field_order=None, use_required_attribute=None, renderer=None)¶

property media¶: Return all media required to render the widgets on this form.

class remapp.forms.MGChartOptionsForm(data=None, files=None, auto_id='id_%s', prefix=None, initial=None, error_class=<class 'django.forms.utils.ErrorList'>, label_suffix=None, empty_permitted=False, field_order=None, use_required_attribute=None, renderer=None)¶

Form for MG chart options

property media¶: Return all media required to render the widgets on this form.

class remapp.forms.MGChartOptionsFormIncStandard(data=None, files=None, auto_id='id_%s', prefix=None, initial=None, error_class=<class 'django.forms.utils.ErrorList'>, label_suffix=None, empty_permitted=False, field_order=None, use_required_attribute=None, renderer=None)¶

property media¶: Return all media required to render the widgets on this form.

class remapp.forms.MergeOnDeviceObserverUIDForm(data=None, files=None, auto_id='id_%s', prefix=None, initial=None, error_class=<class 'django.forms.utils.ErrorList'>, label_suffix=None, empty_permitted=False, field_order=None, use_required_attribute=None, renderer=None)¶

Form for displaying and changing the option for merging on Device Observer UID

property media¶: Return all media required to render the widgets on this form.

class remapp.forms.NMChartOptionsDisplayForm(data=None, files=None, auto_id='id_%s', prefix=None, initial=None, error_class=<class 'django.forms.utils.ErrorList'>, label_suffix=None, empty_permitted=False, field_order=None, use_required_attribute=None, renderer=None)¶

Form for NM chart display options

property media¶: Return all media required to render the widgets on this form.

class remapp.forms.NMChartOptionsForm(data=None, files=None, auto_id='id_%s', prefix=None, initial=None, error_class=<class 'django.forms.utils.ErrorList'>, label_suffix=None, empty_permitted=False, field_order=None, use_required_attribute=None, renderer=None)¶

Form for NM chart options

property media¶: Return all media required to render the widgets on this form.

class remapp.forms.NotPatientIDForm(*args, **kwargs)¶

Form for configuring not-patient ID patterns

property media¶: Return all media required to render the widgets on this form.

class remapp.forms.NotPatientNameForm(*args, **kwargs)¶

Form for configuring not-patient name patterns

property media¶: Return all media required to render the widgets on this form.

class remapp.forms.RFChartOptionsDisplayForm(data=None, files=None, auto_id='id_%s', prefix=None, initial=None, error_class=<class 'django.forms.utils.ErrorList'>, label_suffix=None, empty_permitted=False, field_order=None, use_required_attribute=None, renderer=None)¶

Form for RF chart display options

property media¶: Return all media required to render the widgets on this form.

class remapp.forms.RFChartOptionsDisplayFormIncStandard(data=None, files=None, auto_id='id_%s', prefix=None, initial=None, error_class=<class 'django.forms.utils.ErrorList'>, label_suffix=None, empty_permitted=False, field_order=None, use_required_attribute=None, renderer=None)¶

property media¶: Return all media required to render the widgets on this form.

class remapp.forms.RFChartOptionsForm(data=None, files=None, auto_id='id_%s', prefix=None, initial=None, error_class=<class 'django.forms.utils.ErrorList'>, label_suffix=None, empty_permitted=False, field_order=None, use_required_attribute=None, renderer=None)¶

Form for RF chart options

property media¶: Return all media required to render the widgets on this form.

class remapp.forms.RFChartOptionsFormIncStandard(data=None, files=None, auto_id='id_%s', prefix=None, initial=None, error_class=<class 'django.forms.utils.ErrorList'>, label_suffix=None, empty_permitted=False, field_order=None, use_required_attribute=None, renderer=None)¶

property media¶: Return all media required to render the widgets on this form.

class remapp.forms.RFHighDoseFluoroAlertsForm(*args, **kwargs)¶

Form for displaying and changing fluoroscopy high dose alert settings

property media¶: Return all media required to render the widgets on this form.

class remapp.forms.SizeHeadersForm(my_choice=None, **kwargs)¶

Form for csv column header patient size imports through the web interface

property media¶: Return all media required to render the widgets on this form.

class remapp.forms.SizeUploadForm(data=None, files=None, auto_id='id_%s', prefix=None, initial=None, error_class=<class 'django.forms.utils.ErrorList'>, label_suffix=None, empty_permitted=False, field_order=None, use_required_attribute=None, renderer=None)¶

Form for patient size csv file upload

property media¶: Return all media required to render the widgets on this form.

class remapp.forms.SkinDoseMapCalcSettingsForm(*args, **kwargs)¶

Form for configuring whether skin dose maps are shown / calculated

property media¶: Return all media required to render the widgets on this form.

class remapp.forms.SkinSafeListForm(data=None, files=None, auto_id='id_%s', prefix=None, initial=None, error_class=<class 'django.forms.utils.ErrorList'>, label_suffix=None, empty_permitted=False, instance=None, use_required_attribute=None, renderer=None)¶

Form for adding/updating/removing system from openSkin safe list

property media¶: Return all media required to render the widgets on this form.

class remapp.forms.StandardNameFormBase(data=None, files=None, auto_id='id_%s', prefix=None, initial=None, error_class=<class 'django.forms.utils.ErrorList'>, label_suffix=None, empty_permitted=False, instance=None, use_required_attribute=None, renderer=None)¶

For configuring standard names for study description, requested procedure, procedure and acquisition name.

property media¶: Return all media required to render the widgets on this form.

class remapp.forms.StandardNameFormCT(*args, **kwargs)¶

Form for configuring standard names for study description, requested procedure, procedure and acquisition name

property media¶: Return all media required to render the widgets on this form.

class remapp.forms.StandardNameFormDX(*args, **kwargs)¶

Form for configuring standard names for study description, requested procedure, procedure and acquisition name

property media¶: Return all media required to render the widgets on this form.

class remapp.forms.StandardNameFormMG(*args, **kwargs)¶

Form for configuring standard names for study description, requested procedure, procedure and acquisition name

property media¶: Return all media required to render the widgets on this form.

class remapp.forms.StandardNameFormRF(*args, **kwargs)¶

Form for configuring standard names for study description, requested procedure, procedure and acquisition name

property media¶: Return all media required to render the widgets on this form.

class remapp.forms.StandardNameSettingsForm(*args, **kwargs)¶

Form for configuring whether standard names are shown / used

property media¶: Return all media required to render the widgets on this form.

class remapp.forms.UpdateDisplayNamesForm(data=None, files=None, auto_id='id_%s', prefix=None, initial=None, error_class=<class 'django.forms.utils.ErrorList'>, label_suffix=None, empty_permitted=False, field_order=None, use_required_attribute=None, renderer=None)¶

property media¶: Return all media required to render the widgets on this form.

class remapp.forms.itemsPerPageForm(data=None, files=None, auto_id='id_%s', prefix=None, initial=None, error_class=<class 'django.forms.utils.ErrorList'>, label_suffix=None, empty_permitted=False, field_order=None, use_required_attribute=None, renderer=None)¶

property media¶: Return all media required to render the widgets on this form.

Charts¶

remapp.interface.chart_functions.calc_facet_rows_and_height(df, facet_col_name, facet_col_wrap)¶

Calculates the required total chart height and the number of facet rows. Each row has a hard-coded height of 500 pixels.

Parameters

df – Pandas DataFrame containing the data
facet_col_name – string containing the DataFrame column name containing the facet names
facet_col_wrap – int representing the number of subplots to have on each row

Returns

two-element list containing the chart height in pixels (int) and the number of facet rows (int)

remapp.interface.chart_functions.calc_histogram_bin_data(df, value_col_name, n_bins=10)¶

Calculates histogram bin label text, bin boundaries and bin mid-points

Parameters

df – the Pandas DataFrame containing the data
value_col_name – (string )name of the DataFrame column that contains the values
n_bins – (int) the number of bins to use

Returns

a three element list containing the bin labels, bin boundaries and bin mid-points

remapp.interface.chart_functions.calculate_colour_sequence(scale_name='RdYlBu', n_colours=10)¶

Calculates a sequence of n_colours from the matplotlib colourmap scale_name

Parameters

scale_name – string containing the name of the matplotlib colour scale to use
n_colours – int representing the number of colours required

Returns

list of hexadecimal colours from a matplotlib colormap

remapp.interface.chart_functions.construct_over_time_charts(df, params, group_by_physician=None)¶

Construct a Plotly line chart of average values over time, optionally grouped by performing physician name. For “boxplot” a plotly boxplot of values over time is returned instead of an plotly line chart.

Parameters

df – the Pandas DataFrame containing the data
params – a dictionary of processing parameters
params["df_name_col"] – (string) DataFrame column containing categories
params["name_title"] – (string) name title
params["df_value_col"] – (string) DataFrame column containing values
params["value_title"] – (string) y-axis title
params["df_date_col"] – (string) DataFrame column containing dates
params["date_title"] – (string) date title
params["facet_title"] – (string) subplot title
params["sorting_choice"] – 2-element list. [0] sets sort direction, [1] used to determine which field to sort
params["average_choices"] – list of strings containing requred averages (“mean”, “median”, “boxplot”)
params["time_period"] – string containing the time period to average over; “A” (years), “Q” (quarters), “M” (months), “W” (weeks), “D” (days)
params["grouping_choice"] – (string) “series” or “system”
params["colourmap"] – (string) colourmap to use
params["filename"] – (string) default filename to use for plot bitmap export
params["facet_col_wrap"] – (int) number of subplots per row
params["return_as_dict"] – (boolean) flag to trigger return as a dictionary rather than a HTML DIV
group_by_physician – boolean flag to set whether to group by physician name

Returns

a dictionary containing a combination of [“mean”], [“median”] and [“boxplot”] entries, each of which contains a Plotly figure embedded in an HTML DIV; or Plotly figure as a dictionary (if params[“return_as_dict”] is True); or an error message embedded in an HTML DIV if there was a ValueError when calculating the figure

remapp.interface.chart_functions.create_dataframe(database_events, field_dict, data_point_name_lowercase=None, data_point_name_remove_whitespace_padding=None, data_point_value_multipliers=None, char_wrap=500, uid=None)¶

Creates a Pandas DataFrame from the supplied database records. names fields are made categorical to save system memory Any missing (na) values in names fields are set to Blank

Parameters

database_events – the database events
field_dict – a dictionary of lists, each containing database field names to include in the DataFrame. The dictionary should include “names”, “values”, “dates”, “times” and optionally “system” items
data_point_name_lowercase – boolean flag to determine whether to make all “names” field values lower case
data_point_name_remove_whitespace_padding – boolean flag to determine whether to strip whitespace
data_point_value_multipliers – list of float valuse to multiply each “values” field value by
uid – string containing database field name which contains a unique identifier for each record

Returns

a Pandas DataFrame with a column per required field

remapp.interface.chart_functions.create_dataframe_aggregates(df, df_name_cols, df_agg_col, stats_to_use=None)¶

Creates a Pandas DataFrame with the specified statistics (mean, median, count, for example) grouped by x-ray system name and by the list of provided df_name_cols.

Parameters

df – Pandas DataFrame containing the raw data; it must have an “x_ray_system_name” column
df_name_cols – list of strings representing the DataFrame column names to group by
df_agg_col – string containing the DataFrame column over which to calculate the statistics
stats_to_use – list of strings containing the statistics to calculate, such as “mean”, “median”, “count”

Returns

Pandas DataFrame containing the grouped aggregate data

remapp.interface.chart_functions.create_dataframe_time_series(df, df_name_col, df_value_col, df_date_col='study_date', time_period='M', average_choices=None, group_by_physician=None)¶

Creates a Pandas DataFrame time series of average values grouped by x_ray_system_name and df_name_col

Parameters

df – the Pandas DataFrame containing the raw data
df_name_col – string containing the DataFrame columnn name used to group the data
df_value_col – string containing the DataFrame column containing the values to be averaged
df_date_col – string containing the DataFrame column containing the dates
time_period – string containing the time period to average over; “A” (years), “Q” (quarters), “M” (months), “W” (weeks), “D” (days)
average_choices – list of strings containing one or both of “mean” and “median”
group_by_physician – boolean flag to set whether to group by physician

Returns

Pandas DataFrame containing the time series of average values grouped by system and name

remapp.interface.chart_functions.create_dataframe_weekdays(df, df_name_col, df_date_col='study_date')¶

Creates a Pandas DataFrame of the number of events in each day of the week, and in hour of that day.

Parameters

df – Pandas DataFrame containing the raw data; it must have a “study_time” and “x_ray_system_name” column
df_name_col – string containing the df column name to group the results by
df_date_col – string containing the df column name containing dates

Returns

Pandas DataFrame containing the number of studies per day and hour grouped by name

remapp.interface.chart_functions.create_freq_sorted_category_list(df, df_name_col, sorting)¶

Create a sorted list of categories for frequency charts. Makes use of Pandas DataFrame sort_values (https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.sort_values.html).

sorting[0] sets sort direction

sorting[1] used to determine field to sort on: “name” sorts by df_name_col; otherwise sorted by “x_ray_system_name”

Parameters

df – Pandas DataFrame containing the data
df_name_col – DataFrame column containing the category names
sorting – 2-element list. [0] sets sort direction, [1] used to determine which field to sort on

Returns

dictionary with key df_name_col and a list of sorted categories as the value

remapp.interface.chart_functions.create_sorted_category_list(df, df_name_col, df_value_col, sorting)¶

Create a sorted list of categories for scatter and over-time charts. The data is grouped by df_name_col and the mean and count calculated for each. The grouped DataFrame is then sorted according to the provided sorting. Makes use of Pandas DataFrame sort_values (https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.sort_values.html).

sorting[0] sets sort direction

sorting[1] used to determine sort order: “name” sorts by df_name_col; otherwise sorted by “x_ray_system_name”

Parameters

df – Pandas DataFrame containing the data
df_name_col – DataFrame column containing the category names. Used to group the data
df_value_col – DataFrame column containing values to count and calculate the mean
sorting – 2-element list. [0] sets sort direction, [1] used to determine which field to sort on

Returns

dictionary with key df_name_col and a list of sorted categories as the value

remapp.interface.chart_functions.csv_data_barchart(fig, params)¶

Calculates a Pandas DataFrame containing chart data to be used for csv download

Parameters

fig – Plotly figure containing the data to extract
params – a dictionary of parameters
params["df_name_col"] – (string) DataFrame column containing categories
params["name_axis_title"] – (string) title for the name data
params["value_axis_title"] – (string) title for the value data
params["facet_col"] – (string) DataFrame column used to split data into subgroups

Returns

DataFrame containing the data for download

remapp.interface.chart_functions.csv_data_frequency(fig, params)¶

Calculates a Pandas DataFrame containing chart data to be used for csv download

Parameters

fig – Plotly figure containing the data to extract
params – a dictionary of parameters; must include “x_axis_title”

Returns

DataFrame containing the data for download

remapp.interface.chart_functions.download_link(object_to_download, download_filename, download_link_text='Download csv')¶

Adapted from: https://discuss.streamlit.io/t/heres-a-download-function-that-works-for-dataframes-and-txt/4052

Generates a link to download the given object_to_download.

object_to_download (str, pd.DataFrame): The object to be downloaded. download_filename (str): filename and extension of file. e.g. mydata.csv, some_txt_output.txt download_link_text (str): Text to display for download link.

Examples:

download_link(YOUR_DF, 'YOUR_DF.csv', 'Click here to download data!')

download_link(YOUR_STRING, 'YOUR_STRING.txt', 'Click here to download your text!')

remapp.interface.chart_functions.empty_dataframe_msg(params=None)¶

Returns a string containing an HTML DIV with a message warning that the DataFrame is empty

Parameters: params – parameters which may contain a custom_msg_line
Returns: string containing an html div with the empty DataFrame message

remapp.interface.chart_functions.failed_chart_message_div(custom_msg_line, e)¶

Returns a string containing an HTML DIV with a failed chart message

Parameters

custom_msg_line – string containing a custom line to add to the message
e – Python error object

Returns

string containing the message in an HTML DIV

remapp.interface.chart_functions.global_config(filename, height_multiplier=1.0, height=1080, width=1920)¶

Creates a Plotly global configuration dictionary. The parameters all relate to the chart bitmap that can be saved by the user.

Parameters

filename – string containing the file name to use if the user saves the chart as a graphic file
height_multiplier – floating point value used to scale the chart height
height – int value for the height of the chart graphic file
width – int value for the width of the chart graphic file

Returns

a dictionary of Plotly options

remapp.interface.chart_functions.plotly_barchart(df, params, csv_name='OpenREM chart data.csv')¶

Create a plotly bar chart

Parameters

df – Pandas DataFrame containing the data
params – a dictionary of parameters
params["average_choice"] – (string) DataFrame column containing values (“mean” or “median”)
params["value_axis_title"] – (string) y-axis title
params["df_name_col"] – (string) DataFrame column containing categories
params["name_axis_title"] – (string) x-axis title
params["facet_col"] – (string) DataFrame column used to create subplots
params["facet_col_wrap"] – (int) number of subplots per row
params["sorting_choice"] – 2-element list. [0] sets sort direction, [1] used to determine which field to sort
params["colourmap"] – (string) colourmap to use
params["return_as_dict"] – (boolean) flag to trigger return as a dictionary rather than a HTML DIV
params["filename"] – (string) default filename to use for plot bitmap export
csv_name – (string) default filename to use for plot csv export

Returns

Plotly figure embedded in an HTML DIV; or Plotly figure as a dictionary (if params[“return_as_dict”] is True); or an error message embedded in an HTML DIV if there was a ValueError when calculating the figure

remapp.interface.chart_functions.plotly_barchart_weekdays(df, df_name_col, df_value_col, name_axis_title='', value_axis_title='', colourmap='RdYlBu', filename='OpenREM_workload_chart', facet_col_wrap=3, sorting_choice=None, return_as_dict=False)¶

Create a plotly bar chart of event workload

Parameters

df – Pandas DataFrame containing the data
df_name_col – (string) DataFrame column containing categories
df_value_col – (string) DataFrame column containing values
name_axis_title – (string) x-axis title
value_axis_title – (string) y-axis title
colourmap – (string) colourmap to use
filename – (string) default filename to use for plot bitmap export
facet_col_wrap – (int) number of subplots per row
sorting_choice – 2-element list. [0] sets sort direction, [1] used to determine which field to sort
return_as_dict – (boolean) flag to trigger return as a dictionary rather than a HTML DIV

Returns

Plotly figure embedded in an HTML DIV; or Plotly figure as a dictionary (if “return_as_dict” is True); or an error message embedded in an HTML DIV if there was a ValueError when calculating the figure

remapp.interface.chart_functions.plotly_binned_statistic_barchart(df, params)¶

Create a plotly binned statistic bar chart

Parameters

df – Pandas DataFrame containing the data
params – a dictionary of parameters
params["df_category_col"] – (string) DataFrame column containing categories
params["df_facet_col"] – (string) DataFrame column used to create subplots
params["facet_title"] – (string) Subplot title
params["facet_col_wrap"] – (int) number of subplots per row
params["user_bins"] – list of ints containing bin edges for binning
params["df_category_col"] – (string) DataFrame column containing categories
params["df_x_value_col"] – (string) DataFrame column containing x data
params["df_y_value_col"] – (string) DataFrame column containing y data
params["x_axis_title"] – (string) Title for x-axis
params["y_axis_title"] – (string) Title for y-axis
params["stat_name"] – (string) “mean” or “median”
params["sorting_choice"] – 2-element list. [0] sets sort direction, [1] used to determine which field to sort
params["colourmap"] – (string) colourmap to use
params["return_as_dict"] – (boolean) flag to trigger return as a dictionary rather than a HTML DIV
params["filename"] – (string) default filename to use for plot bitmap export

Returns

Plotly figure embedded in an HTML DIV; or Plotly figure as a dictionary (if params[“return_as_dict”] is True); or an error message embedded in an HTML DIV if there was a ValueError when calculating the figure

remapp.interface.chart_functions.plotly_boxplot(df, params)¶

Produce a plotly boxplot

Parameters

df – Pandas DataFrame containing the data
params – a dictionary of parameters
params["df_value_col"] – (string) DataFrame column containing values
params["value_axis_title"] – (string) x-axis title
params["df_name_col"] – (string) DataFrame column containing categories
params["name_axis_title"] – (string) y-axis title
params["df_facet_col"] – (string) DataFrame column used to create subplots
params["df_facet_col_wrap"] – (int) number of subplots per row
params["sorting_choice"] – 2-element list. [0] sets sort direction, [1] used to determine which field to sort
params["colourmap"] – (string) colourmap to use
params["return_as_dict"] – (boolean) flag to trigger return as a dictionary rather than a HTML DIV

Returns

Plotly figure embedded in an HTML DIV; or Plotly figure as a dictionary (if params[“return_as_dict”] is True); or an error message embedded in an HTML DIV if there was a ValueError when calculating the figure

remapp.interface.chart_functions.plotly_frequency_barchart(df, params, csv_name='OpenREM chart data.csv')¶

Create a plotly bar chart of event frequency

Parameters

df – Pandas DataFrame containing the data
params – a dictionary of parameters
params["df_x_axis_col"] – (string) DataFrame column containing categories
params["x_axis_title"] – (string) x-axis title
params["groupby_cols"] – list of strings with DataFrame columns to group data by
params["grouping_choice"] – (string) “series” or “system”
params["sorting_choice"] – 2-element list. [0] sets sort direction, [1] used to determine which field to sort
params["legend_title"] – (string) legend title
params["facet_col"] – (string) DataFrame column used to create subplots
params["facet_col_wrap"] – (int) number of subplots per row
params["return_as_dict"] – (boolean) flag to trigger return as a dictionary rather than a HTML DIV
params["colourmap"] – (string) colourmap to use
params["filename"] – (string) default filename to use for plot bitmap export
csv_name – (string) default filename to use for plot csv export

Returns

Plotly figure embedded in an HTML DIV; or Plotly figure as a dictionary (if “return_as_dict” is True); or an error message embedded in an HTML DIV if there was a ValueError when calculating the figure

remapp.interface.chart_functions.plotly_histogram_barchart(df, params)¶

Create a plotly histogram bar chart

Parameters

df – Pandas DataFrame containing the data
params – a dictionary of parameters
params["df_value_col"] – (string) DataFrame column containing values
params["value_axis_title"] – (string) y-axis title
params["df_facet_col"] – (string) DataFrame column used to create subplots
params["df_category_name_list"] – string list of each category name
params["df_facet_col_wrap"] – (int) number of subplots per row
params["n_bins"] – (int) number of hisgogram bins to use
params["colourmap"] – (string) colourmap to use
params["sorting_choice"] – 2-element list. [0] sets sort direction, [1] used to determine which field to sort
params["global_max_min"] – (boolean) flag to calculate global max and min or per-subplot max and min
params["legend_title"] – (string) legend title
params["return_as_dict"] – (boolean) flag to trigger return as a dictionary rather than a HTML DIV
params["filename"] – (string) default filename to use for plot bitmap export

Returns

Plotly figure embedded in an HTML DIV; or Plotly figure as a dictionary (if params[“return_as_dict”] is True); or an error message embedded in an HTML DIV if there was a ValueError when calculating the figure

remapp.interface.chart_functions.plotly_scatter(df, params)¶

Create a plotly scatter chart

Parameters

df – Pandas DataFrame containing the data
params – a dictionary of parameters
params["df_name_col"] – (string) DataFrame column containing categories
params["df_x_col"] – (string) DataFrame column containing x values
params["df_y_col"] – (string) DataFrame column containing y values
params["sorting_choice"] – 2-element list. [0] sets sort direction, [1] used to determine which field to sort
params["grouping_choice"] – (string) “series” or “system”
params["legend_title"] – (string) legend title
params["facet_col_wrap"] – (int) number of subplots per row
params["colourmap"] – (string) colourmap to use
params["x_axis_title"] – (string) x-axis title
params["y_axis_title"] – (string) y-axis title
params["filename"] – (string) default filename to use for plot bitmap export
params["return_as_dict"] – (boolean) flag to trigger return as a dictionary rather than a HTML DIV

Returns

Plotly figure embedded in an HTML DIV; or Plotly figure as a dictionary (if “return_as_dict” is True); or an error message embedded in an HTML DIV if there was a ValueError when calculating the figure

remapp.interface.chart_functions.plotly_set_default_theme(theme_name)¶

A short method to set the plotly chart theme

Parameters: theme_name – the name of the theme
Returns

remapp.interface.chart_functions.plotly_timeseries_linechart(df, params)¶

Create a plotly line chart of data over time

Parameters

df – Pandas DataFrame containing the data
params – a dictionary of parameters
params["df_facet_col"] – (string) DataFrame column used to create subplots
params["df_facet_col_wrap"] – (int) number of subplots per row
params["facet_title"] – (string) subplot title
params["df_value_col"] – (string) DataFrame column containing values
params["value_axis_title"] – (string) y-axis title
params["colourmap"] – (string) colourmap to use
params["colourmap"] – (string) colourmap to use
params["df_date_col"] – (string) DataFrame column containing dates
params["df_count_col"] – (string) DataFrame column containing frequency data
params["df_name_col"] – (string) DataFrame column containing categories
params["legend_title"] – (string) legend title
params["name_axis_title"] – (string) x-axis title
params["return_as_dict"] – (boolean) flag to trigger return as a dictionary rather than a HTML DIV
params["filename"] – (string) default filename to use for plot bitmap export

Returns

Plotly figure embedded in an HTML DIV; or Plotly figure as a dictionary (if “return_as_dict” is True); or an error message embedded in an HTML DIV if there was a ValueError when calculating the figure

remapp.interface.chart_functions.save_fig_as_html_div(fig, filename, active=False)¶

Saves the Plotly figure as an HTML file containing a single DIV. The file is saved on the OpenREM server in MEDIA_ROOTchartsyyyymmdd. Viewing the saved file requires an active internet connection as the Plotly JavaScript library is not included in the file.

This method is not currently accessible to an OpenREM user or administrator - it is present to assist developers when producing example charts for the OpenREM documentation. It must be manually activated by setting active=True in the method definition.

Parameters

fig – a Plotly figure
filename – (string )the filename to use
active – (boolean) to set whether to save the figure

DICOM networking modules¶

Query-retrieve module¶

Query function¶

openrem.remapp.netdicom.qrscu.qrscu(qr_scp_pk=None, store_scp_pk=None, implicit=False, explicit=False, move=False, query_id=None, date_from=None, date_until=None, single_date=False, time_from=None, time_until=None, modalities=None, inc_sr=False, remove_duplicates=True, filters=None, get_toshiba_images=False, get_empty_sr=False)¶

Query retrieve service class user function

Queries a pre-configured remote query retrieve service class provider for dose metric related objects, making use of the filter parameters provided. Can automatically trigger a c-move (retrieve) operation.

Parameters

qr_scp_pk (int, optional) – Database ID/pk of the remote QR SCP (Default value = None)
store_scp_pk (int, optional) – Database ID/pk of the local store SCP (Default value = None)
implicit (bool, optional) – Prefer implicit transfer syntax (preference possibly not implemented) (Default value = False)
explicit (bool, optional) – Prefer explicit transfer syntax (preference possibly not implemented) (Default value = False)
move (bool, optional) – Automatically trigger move request when query is complete (Default value = False)
query_id (str, optional) – UID of query if generated by web interface (Default value = None)
date_from (str, optional) – Date to search from, format yyyy-mm-dd (Default value = None)
date_until (str, optional) – Date to search until, format yyyy-mm-dd (Default value = None)
single_date (bool, optional) – search only on date_from, allows time_from/time_until (Default value = False)
time_from (str, optional) – Time of day to search from, format hhmm 24 hour clock, single date only (Default value = None)
time_until (str, optional) – Time of day to search until, format hhmm 24 hour clock, single date only (Default value = None)
modalities (list, optional) – Modalities to search for, options are CT, MG, DX and FL (Default value = None)
inc_sr (bool, optional) – Only include studies that only have structured reports in (unknown modality) (Default value = False)
remove_duplicates (bool, optional) – If True, studies that already exist in the database are removed from the query results (Default value = True)
filters (dictionary list, optional) – lowercase include and exclude lists for StationName and StudyDescription (Default value = None)
get_toshiba_images (bool, optional) – Whether to try to get Toshiba dose summary images
get_empty_sr (bool, optional) – Whether to get SR series that return nothing at image level

Returns

Series Instance UIDs are stored as rows in the database to be used by a move request. Move request is optionally triggered automatically.

Move function¶

openrem.remapp.netdicom.qrscu.movescu(query_id)¶: C-Move request element of query-retrieve service class user :param query_id: ID of query in the DicomQuery table :return: None

openrem_qr.py script¶

Query remote server and retrieve to OpenREM

usage: openrem_qr.py [-h] [-ct] [-mg] [-fl] [-dx] [-nm] [-f yyyy-mm-dd]
                     [-t yyyy-mm-dd] [-sd yyyy-mm-dd] [-tf hhmm] [-tt hhmm]
                     [-e string] [-i string] [-sne string] [-sni string]
                     [--stationname_study_level] [-toshiba] [-sr] [-dup]
                     [-emptysr]
                     qr_id store_id

Positional Arguments¶

qr_id: Database ID of the remote QR node
store_id: Database ID of the local store node

Named Arguments¶

-ct

Query for CT studies. Cannot be used with -sr

Default: False

-mg

Query for mammography studies. Cannot be used with -sr

Default: False

-fl

Query for fluoroscopy studies. Cannot be used with -sr

Default: False

-dx

Query for planar X-ray studies (includes panoramic X-ray studies). Cannot be used with -sr

Default: False

-nm

Query for nuclear medicine studies. Cannot be used with -sr

Default: False

-f, --dfrom

Date from, format yyyy-mm-dd. Cannot be used with –single_date

-t, --duntil

Date until, format yyyy-mm-dd. Cannot be used with –single_date

-sd, --single_date

Date, format yyy-mm-dd. Cannot be used with –dfrom or –duntil

-tf, --tfrom

Time from, format hhmm. Requires –single_date.

-tt, --tuntil

Time until, format hhmm. Requires –single_date.

-e, --desc_exclude

Terms to exclude in study description, comma separated, quote whole string

-i, --desc_include

Terms that must be included in study description, comma separated, quote whole string

-sne, --stationname_exclude

Terms to exclude in station name, comma separated, quote whole string

-sni, --stationname_include

Terms to include in station name, comma separated, quote whole string

--stationname_study_level

Advanced: Filter station name at Study level, instead of at Series level

Default: False

-toshiba

Advanced: Attempt to retrieve CT dose summary objects and one image from each series

Default: False

-sr

Advanced: Use if store has RDSRs only, no images. Cannot be used with -ct, -mg, -fl, -dx

Default: False

-dup

Advanced: Retrieve duplicates (objects that have been processed before)

Default: False

-emptysr

Advanced: Get SR series that return nothing at image level query

Default: False

NetDICOM common functions¶

openrem.remapp.netdicom.tools.echoscu(scp_pk=None, store_scp=False, qr_scp=False)¶: Function to check if built-in Store SCP or remote Query-Retrieve SCP returns a DICOM echo :param scp_pk: Primary key if either Store or QR SCP in database :param store_scp: True if checking Store SCP :param qr_scp: True if checking QR SCP :return: ‘AssocFail’, Success or ?

BackgroundTask¶

Contains functions for running tasks in the background.

remapp.tools.background.get_current_task()¶

Returns: The associated BackgroundTask object when called in a task. If this is not executed in a background Task None will be returned.

remapp.tools.background.get_or_generate_task_uuid()¶

Returns: If called from within a task the task id, else a generated uuid

remapp.tools.background.get_queued_tasks(task_type=None) → List[QueuedTask]¶

Returns all task which are currently waiting for execution

Parameters: task_type – Optionally filter by task type
Returns: List of queued tasks

remapp.tools.background.record_task_error_exit(error_msg)¶: Small helper that checks if we are in a task and assuming we are records error_msg as well as setting the completed_successfully to false and completed to True. Note that get_current_task will return None after a call to this.

remapp.tools.background.record_task_info(info_msg)¶: Small helper that checks if we are in a task and assuming we are records info_msg as info.

remapp.tools.background.record_task_related_query(study_instance_uid)¶: Tries to find the related DicomQRRspStudy object given a study instance uid and if this is running in a task will record it to the query object. This is used to later find the import tasks that were run as part of a query. Since this actually just takes the latest query if the user runs imports manually via script it may in principle wrongly associate.

remapp.tools.background.remove_task_from_queue(task_id: str)¶

Removes task from queue.

Parameters: task_id – task id of the task in question

remapp.tools.background.run_in_background(func, task_type, *args, **kwargs) → Result¶

Syntactic sugar around run_in_background_with_limits.

Arguments correspond to run_in_background_with_limits. Will always run the passed function as fast as possible. This function should always be used for functions triggered from the webinterface.

remapp.tools.background.run_in_background_with_limits(func, task_type, num_proc, num_of_task_type, *args, **kwargs) → Result¶

Runs func as background Process.

This method will create a new task which will be scheduled to be run by the Huey consumer with the specified priority (defaults to 0). The priority can be passed via a keyword argument

Internally the Huey consumer spawns a new process, which then creates a BackgroundTask object. This can be obtained via get_current_task() inside the calling process. Note that BackgroundTask objects will not be deleted onto completion - instead the complete flag will be set to True.

num_proc and num_of_task_type can be used to give conditions on the start.

Parameters

func – The function to run. Note that you should set the status of the task yourself and mark as completed when exiting yourself e.g. via sys.exit(). Assuming the function returns normally on success or returns with an exception on error, the status of the BackgroundTask object will be set correctly.
task_type – One of the strings declared in BackgroundTask.task_type. Indicates which kind of background process this is supposed to be. (E.g. move, query, …)
num_proc – Will wait with execution until there are less than num_proc active tasks. If num_proc == 0 will run directly
num_of_task_type – A dictionary from str to int, where key should be some used task_type. Will wait until there are less active tasks of task_type than specified in the value of the dict for that key.
args – Positional arguments. Passed to func.
kwargs – Keywords arguments. Passed to func.

Returns

The BackgroundTask object.

remapp.tools.background.terminate_background(task: BackgroundTask)¶: Terminate a background task by force. Sets complete=True on the task object.

remapp.tools.background.wait_task(task: Result)¶: Wait until the task has completed

Adding new charts¶

To add a new chart several files need to be updated:

models.py
forms.py
views.py
xxfiltered.html
xxChartAjax.js
displaychartoptions.html

Where xx is one of ct, dx, mg or rf

The additions to the files add:

database fields in the user profile to control whether the new charts are plotted (models.py)
new options on the chart plotting forms (forms.py, displaychartoptions.html)
extra code to calculate the data for the new charts if they are switched on (views.py)
a section of html and JavaScript to contain the charts (xxfiltered.html)
a section of JavaScript to pass the data calculated by views.py to xxfiltered.html

The process is probably best illustrated with an example. What follows is a description of how to add a new chart that displays study workload for fluoroscopy, and a pie chart of study description frequency.

Additions to `models.py`¶

A field per chart needs to be added to the UserProfile section in models.py to control whether the new charts should be plotted. There is a section of this file that looks like the following:

plotCTAcquisitionMeanDLP = models.BooleanField(default=True)
plotCTAcquisitionMeanCTDI = models.BooleanField(default=True)
plotCTAcquisitionFreq = models.BooleanField(default=False)
plotCTStudyMeanDLP = models.BooleanField(default=True)
plotCTStudyFreq = models.BooleanField(default=False)
plotCTRequestMeanDLP = models.BooleanField(default=False)
plotCTRequestFreq = models.BooleanField(default=False)
plotCTStudyPerDayAndHour = models.BooleanField(default=False)
plotCTStudyMeanDLPOverTime = models.BooleanField(default=False)
plotCTStudyMeanDLPOverTimePeriod = models.CharField(max_length=6,
                                                    choices=TIME_PERIOD,
                                                    default=MONTHS)
plotCTInitialSortingChoice = models.CharField(max_length=4,
                                              choices=SORTING_CHOICES_CT,
                                              default=FREQ)

Two new lines needs to be added to this section, using appropriate names such as:

plotRFStudyPerDayAndHour = models.BooleanField(default=False)
plotRFStudyFreq = models.BooleanField(default=False)

Adding new fields to models.py requires that a database migration is carried out to add the fields to the database. This is done via the command line:

python manage.py makemigrations remapp
python manage.py migrate remapp

The first command should result in a response similar to:

Migrations for 'remapp':
  0004_auto_20160424_1116.py:
    - Add field plotRFAcquisitionCTDIOverTime to userprofile
    - Add field plotRFStudyFreq to userprofile

The second command should result in a response similar to:

Operations to perform:
  Apply all migrations: remapp
Running migrations:
  Rendering model states... DONE
  Applying remapp.0004_auto_20160424_1116... OK

That’s the end of the changes required in models.py

Additions to `forms.py`¶

Two additional lines need to be added to the XXChartOptionsForm and XXChartOptionsDisplayForm methods in forms.py, where XX is one of CT, DX, MG or RF.

For our new charts the following lines needs to be added to both RFChartOptionsForm and RFChartOptionsDisplayForm:

plotRFStudyPerDayAndHour = forms.BooleanField(label='Study workload', required=False)
plotRFStudyFreq = forms.BooleanField(label='Study frequency', required=False)

In addition, a new method needs to be added so that the RF chart options are shown when the user goes to Config -> Chart options:

class RFChartOptionsDisplayForm(forms.Form):
    plotRFStudyPerDayAndHour = forms.BooleanField(label='Study workload', required=False)
    plotRFStudyFreq = forms.BooleanField(label='Study frequency', required=False)

That’s the end of the changes required in forms.py

Additions to `views.py`¶

Four methods in this file need to be updated.

`xx_summary_list_filter` additions¶

Some additions need to be made to the xx_summary_list_filter method in views.py, where xx is one of ct, dx, mg or rf. As we’re adding new RF charts, we need to edit rf_summary_list_filter.

A section of this method examines the user’s chart plotting preferences. Code must be added to include the new chart in these checks. An abbreviated version of the section is shown below.

# Obtain the chart options from the request
chart_options_form = RFChartOptionsForm(request.GET)
# Check whether the form data is valid
if chart_options_form.is_valid():
    # Use the form data if the user clicked on the submit button
    if "submit" in request.GET:
        # process the data in form.cleaned_data as required
        user_profile.plotCharts = chart_options_form.cleaned_data['plotCharts']
        if median_available:
            user_profile.plotAverageChoice = chart_options_form.cleaned_data['plotMeanMedianOrBoth']
        user_profile.save()

    else:
        form_data = {'plotCharts': user_profile.plotCharts,
                     'plotMeanMedianOrBoth': user_profile.plotAverageChoice}
        chart_options_form = RFChartOptionsForm(form_data)

Two new lines needs to be inserted into the if and else sections for the new chart:

# Obtain the chart options from the request
chart_options_form = RFChartOptionsForm(request.GET)
# Check whether the form data is valid
if chart_options_form.is_valid():
    # Use the form data if the user clicked on the submit button
    if "submit" in request.GET:
        # process the data in form.cleaned_data as required
        user_profile.plotCharts = chart_options_form.cleaned_data['plotCharts']
        user_profile.plotRFStudyPerDayAndHour = chart_options_form.cleaned_data['plotRFStudyPerDayAndHour']
        user_profile.plotRFStudyFreq = chart_options_form.cleaned_data['plotRFStudyFreq']
        if median_available:
            user_profile.plotAverageChoice = chart_options_form.cleaned_data['plotMeanMedianOrBoth']
        user_profile.save()

    else:
        form_data = {'plotCharts': user_profile.plotCharts,
                     'plotRFStudyPerDayAndHour': user_profile.plotRFStudyPerDayAndHour,
                     'plotRFStudyFreq': user_profile.plotRFStudyFreq,
                     'plotMeanMedianOrBoth': user_profile.plotAverageChoice}
        chart_options_form = RFChartOptionsForm(form_data)

`xx_summary_chart_data` additions¶

The return_structure variable needs the new user_profile fields adding.

Before:

return_structure =\
    rf_plot_calculations(f, request_results, median_available, user_profile.plotAverageChoice,
                         user_profile.plotSeriesPerSystem, user_profile.plotHistogramBins,
                         user_profile.plotHistograms)

After:

return_structure =\
    rf_plot_calculations(f, request_results, median_available, user_profile.plotAverageChoice,
                         user_profile.plotSeriesPerSystem, user_profile.plotHistogramBins,
                         user_profile.plotRFStudyPerDayAndHour,  user_profile.plotRFStudyFreq,
                         user_profile.plotHistograms)

`xx_plot_calculations` additions¶

Two items needs to be added to this method’s parameters.

Before:

def rf_plot_calculations(f, request_results, median_available, plot_average_choice, plot_series_per_systems,
                         plot_histogram_bins, plot_histograms):

After:

def rf_plot_calculations(f, request_results, median_available, plot_average_choice, plot_series_per_systems,
                         plot_histogram_bins, plot_study_per_day_and_hour, plot_study_freq, plot_histograms):

Our new charts makes use of study_events (rather than acquisition_events or request_events). We therefore need to ensure that study_events are available if the user has chosen to show the new chart.

After additions:

if plot_study_per_day_and_hour:
    study_events = f.qs

We now need to add code that will calculate the data for the new charts. This uses one of the methods in the chart_functions.py file, located in the interface folder of the OpenREM project.

if plot_study_per_day_and_hour:
    result = workload_chart_data(study_events)
    return_structure['studiesPerHourInWeekdays'] = result['workload']

if plot_study_freq:
    result = average_chart_inc_histogram_data(study_events,
                                              'generalequipmentmoduleattr__unique_equipment_name_id__display_name',
                                              'study_description',
                                              'projectionxrayradiationdose__accumxraydose__accumintegratedprojradiogdose__dose_area_product_total',
                                              1000000,
                                              plot_study_dap, plot_study_freq,
                                              plot_series_per_systems, plot_average_choice,
                                              median_available, plot_histogram_bins,
                                              calculate_histograms=plot_histograms)

    return_structure['studySystemList'] = result['system_list']
    return_structure['studyNameList'] = result['series_names']
    return_structure['studySummary'] = result['summary']

The data in return_structure will now be available to the browser via JavaScript, and can be used to populate the charts themselves.

`chart_options_view` additions¶

The RF options form need to be imported

Before:

from remapp.forms import GeneralChartOptionsDisplayForm, DXChartOptionsDisplayForm, CTChartOptionsDisplayForm

After:

from remapp.forms import GeneralChartOptionsDisplayForm, DXChartOptionsDisplayForm, CTChartOptionsDisplayForm,\
    RFChartOptionsDisplayForm

The RF form items need to be included

Before (abbreviated):

if request.method == 'POST':
    general_form = GeneralChartOptionsDisplayForm(request.POST)
    ct_form = CTChartOptionsDisplayForm(request.POST)
    dx_form = DXChartOptionsDisplayForm(request.POST)
    if general_form.is_valid() and ct_form.is_valid() and dx_form.is_valid() and rf_form.is_valid():
        try:
            # See if the user has plot settings in userprofile
            user_profile = request.user.userprofile
        except:
            # Create a default userprofile for the user if one doesn't exist
            create_user_profile(sender=request.user, instance=request.user, created=True)
            user_profile = request.user.userprofile

        user_profile.plotCharts = general_form.cleaned_data['plotCharts']
        ...
        ...
        user_profile.plotHistogramBins = general_form.cleaned_data['plotHistogramBins']

        user_profile.plotCTAcquisitionMeanDLP = ct_form.cleaned_data['plotCTAcquisitionMeanDLP']
        ...
        ...
        user_profile.plotCTInitialSortingChoice = ct_form.cleaned_data['plotCTInitialSortingChoice']

        user_profile.plotDXAcquisitionMeanDAP = dx_form.cleaned_data['plotDXAcquisitionMeanDAP']
        ...
        ...
        user_profile.plotDXInitialSortingChoice = dx_form.cleaned_data['plotDXInitialSortingChoice']

        user_profile.save()

    messages.success(request, "Chart options have been updated")

...
...

general_form_data = {'plotCharts': user_profile.plotCharts,
                     'plotMeanMedianOrBoth': user_profile.plotAverageChoice,
                     'plotInitialSortingDirection': user_profile.plotInitialSortingDirection,
                     'plotSeriesPerSystem': user_profile.plotSeriesPerSystem,
                     'plotHistogramBins': user_profile.plotHistogramBins}

ct_form_data = {'plotCTAcquisitionMeanDLP': user_profile.plotCTAcquisitionMeanDLP,
                ...
                ...
                'plotCTInitialSortingChoice': user_profile.plotCTInitialSortingChoice}

dx_form_data = {'plotDXAcquisitionMeanDAP': user_profile.plotDXAcquisitionMeanDAP,
                ...
                ...
                'plotDXInitialSortingChoice': user_profile.plotDXInitialSortingChoice}


general_chart_options_form = GeneralChartOptionsDisplayForm(general_form_data)
ct_chart_options_form = CTChartOptionsDisplayForm(ct_form_data)
dx_chart_options_form = DXChartOptionsDisplayForm(dx_form_data)

return_structure = {'admin': admin,
                    'GeneralChartOptionsForm': general_chart_options_form,
                    'CTChartOptionsForm': ct_chart_options_form,
                    'DXChartOptionsForm': dx_chart_options_form
                    }

After (abbreviated):

if request.method == 'POST':
    general_form = GeneralChartOptionsDisplayForm(request.POST)
    ct_form = CTChartOptionsDisplayForm(request.POST)
    dx_form = DXChartOptionsDisplayForm(request.POST)
    rf_form = RFChartOptionsDisplayForm(request.POST)
    if general_form.is_valid() and ct_form.is_valid() and dx_form.is_valid() and rf_form.is_valid():
        try:
            # See if the user has plot settings in userprofile
            user_profile = request.user.userprofile
        except:
            # Create a default userprofile for the user if one doesn't exist
            create_user_profile(sender=request.user, instance=request.user, created=True)
            user_profile = request.user.userprofile

        user_profile.plotCharts = general_form.cleaned_data['plotCharts']
        ...
        ...
        user_profile.plotHistogramBins = general_form.cleaned_data['plotHistogramBins']

        user_profile.plotCTAcquisitionMeanDLP = ct_form.cleaned_data['plotCTAcquisitionMeanDLP']
        ...
        ...
        user_profile.plotCTInitialSortingChoice = ct_form.cleaned_data['plotCTInitialSortingChoice']

        user_profile.plotDXAcquisitionMeanDAP = dx_form.cleaned_data['plotDXAcquisitionMeanDAP']
        ...
        ...
        user_profile.plotDXInitialSortingChoice = dx_form.cleaned_data['plotDXInitialSortingChoice']

        user_profile.plotRFStudyPerDayAndHour = rf_form.cleaned_data['plotRFStudyPerDayAndHour']
        user_profile.plotRFStudyFreq = rf_form.cleaned_data['plotRFStudyFreq']

        user_profile.save()

    messages.success(request, "Chart options have been updated")

...
...

general_form_data = {'plotCharts': user_profile.plotCharts,
                     ...
                     ...
                     'plotHistogramBins': user_profile.plotHistogramBins}

ct_form_data = {'plotCTAcquisitionMeanDLP': user_profile.plotCTAcquisitionMeanDLP,
                ...
                ...
                'plotCTInitialSortingChoice': user_profile.plotCTInitialSortingChoice}

dx_form_data = {'plotDXAcquisitionMeanDAP': user_profile.plotDXAcquisitionMeanDAP,
                ...
                ...
                'plotDXInitialSortingChoice': user_profile.plotDXInitialSortingChoice}

rf_form_data = {'plotDXStudyPerDayAndHour': user_profile.plotDXStudyPerDayAndHour,
                'plotRFStudyFreq': user_profile.plotRFStudyFreq}

general_chart_options_form = GeneralChartOptionsDisplayForm(general_form_data)
ct_chart_options_form = CTChartOptionsDisplayForm(ct_form_data)
dx_chart_options_form = DXChartOptionsDisplayForm(dx_form_data)
rf_chart_options_form = RFChartOptionsDisplayForm(rf_form_data)

return_structure = {'admin': admin,
                    'GeneralChartOptionsForm': general_chart_options_form,
                    'CTChartOptionsForm': ct_chart_options_form,
                    'DXChartOptionsForm': dx_chart_options_form,
                    'RFChartOptionsForm': rf_chart_options_form,
                    }

Additions to `displaychartoptions.html`¶

A new div needs to be added for the fluoroscopy chart options:

<div class="panel-heading">
  <h3 class="panel-title">Fluoroscopy chart options</h3>
</div>
<div class="panel-body">
  <table>
    {% csrf_token %}
    {{ RFChartOptionsForm }}
  </table>
  <input class="btn btn-default" name="submit" type="submit" />
</div>

Additions to `rffiltered.html`¶

A section of this file sets a JavaScript variable per chart. Two new ones needs to be added.

Additions:

{% if request.user.userprofile.plotRFStudyPerDayAndHour %}
    <script>
        var plotRFStudyPerDayAndHour = true;
        result = chartWorkload('piechartStudyWorkloadDIV', 'Studies');
    </script>
{% endif %}

{% if request.user.userprofile.plotRFStudyFreq %}
    <script>
        var plotRFStudyFreq = true;
        var urlStartStudy = '/openrem/rf/?{% for field in filter.form %}{% if field.name != 'study_description' and field.name != 'o' and field.value %}&{{ field.name }}={{ field.value }}{% endif %}{% endfor %}&study_description=';
        result = chartFrequency('piechartStudyDIV', 'Study description frequency');
    </script>
{% endif %}

A second section of code needs to be added to rffiltered.html to include a DIV for the new charts:

{% if request.user.userprofile.plotRFStudyPerDayAndHour %}
    <!-- HTML to include div container for study workload -->

    <script>
        $(window).resize(function() {
            chartSetExportSize('piechartStudyWorkloadDIV');
            fitChartToDiv('piechartStudyWorkloadDIV');
        });
    </script>

    <div class="panel-group" id="accordion5">
        <div class="panel panel-default">
            <div class="panel-heading">
                <h4 class="panel-title">
                    <a data-toggle="collapse" data-parent="#accordion5" href="#collapseStudyWorkloadPieChart" onclick="setTimeout(function() {$(document).resize();}, 0);">
                        Pie chart showing a breakdown of number of studies per weekday.
                    </a>
                </h4>
            </div>
            <div id="collapseStudyWorkloadPieChart" class="panel-collapse collapse">
                <div class="panel-body">
                    <div id="piechartStudyWorkloadDIV" style="height: auto; margin: 0 0"></div>
                    <p>Click on a segment to be taken to a pie chart showing the breakdown per hour for that weekday.</p>
                    <a onclick="enterFullScreen('collapseStudyWorkloadPieChart', 'piechartStudyWorkloadDIV')" class="btn btn-default btn-sm" role="button">Toggle fullscreen</a>
                </div>
            </div>
        </div>
    </div>
    <!-- End of HTML to include div container for studies per week day pie chart -->
{% endif %}

{% if request.user.userprofile.plotRFStudyFreq %}
    <!-- HTML to include div container for study name pie chart -->

    <script>
        $(window).resize(function() {
            chartSetExportSize('piechartStudyDIV');
            fitChartToDiv('piechartStudyDIV');
        });
    </script>

    <div class="panel-group" id="accordionPiechartStudy">
        <div class="panel panel-default">
            <div class="panel-heading">
                <h4 class="panel-title">
                    <a data-toggle="collapse" data-parent="#accordionPiechartStudy" href="#collapseStudyPieChart" onclick="setTimeout(function() {$(document).resize();}, 0);">
                        Pie chart showing a breakdown of study name frequency.
                    </a>
                </h4>
            </div>
            <div id="collapseStudyPieChart" class="panel-collapse collapse">
                <div class="panel-body">
                    <div id="piechartStudyDIV" style="height: auto; margin: 0 0"></div>
                    <a onclick="enterFullScreen('collapseStudyPieChart', 'piechartStudyDIV')" class="btn btn-default btn-sm" role="button">Toggle fullscreen</a>
                </div>
            </div>
        </div>
    </div>
    <!-- End of HTML to include div container for study name pie chart -->
{% endif %}

Additions to `rfChartAjax.js`¶

This file needs to update the skeleton chart with the data that has been provided by views.py. It does this via the appropriate routines contained in the chartUpdateData.js file. In this case, updateWorkloadChart and updateFrequencyChart:

// Study workload chart data
if(typeof plotRFStudyPerDayAndHour !== 'undefined') {
    updateWorkloadChart(json.studiesPerHourInWeekdays, 'piechartStudyWorkloadDIV', colour_scale);
}

// Study description frequency chart data start
if(typeof plotRFStudyFreq !== 'undefined') {
    updateFrequencyChart(json.studyNameList, json.studySystemList, json.studySummary, urlStartStudy, 'piechartStudyDIV', colour_scale);
}

That’s it - you should now have two new charts visible in the fluoroscopy filtered page.

Indices and tables¶

Release Notes and Change Log¶

Version history change log¶

OpenREM version history¶

1.0.0b2 (2023-04-20)¶

#990 SkinDose: fixed bug where a zero study dap causes a TypeError in the e-mail sending code

#988 Imports: fixing hard-written DX text in MG import messages

#987 Tasks: configuring Windows WinSW log files to roll over to ensure their size is restricted

#986 SkinDose: fixed bug where a zero study dap causes a divide by zero error

#984 Interface: improved performance of home page

#981 Interface: reorganised the config menu

#980 Interface: improved standard name form layout

#977 Documentation: fix issues preventing translations to be built

#976 NM charts: Fixed bug where graphs were not displayed correctly for some chart options

#972 DICOM Networking: better analysis of SR series with multiple SOP Class UIDs

#971 Imports: fixing NM image index error

#970 Emails: fixed formatting of high fluoro dose alert emails and included peak skin dose for each study in time delta

#967 Imports: made total DLP optional for Philips CT imports

#965 SkinDose: fixed bug where a zero study dap causes a divide by zero error

#964 Tasks: restricted default task history to 2000 stored entries; enabled admin users to configure this value

#962 Documentation: updated packages to make bullet points work again

#960 SkinDose: fixed bug where multiple entries in the openskin safelist table with different software versions caused an error

#959 Installation: upgraded packages to latest versions

#958 Interface: fixed task table sorting for Started column

#957 Imports: fixed DX extractor total_number_of_radiographic_frames is NoneType error

#956 Interface: enable updating of standard name database links

#955 Imports: fixed error when importing RDSR with empty DAP measured value sequence

#953 Interface: updated “DX and CR” to “Radiography”, and “Radiographic” to “Radiography”

#950 Documentation: removed (outdated) instructions for Conquest

#949 Documentation: updated the upgrade instructions from older versions

#947 Tests: enforce ordering within fluoro exposure sets and specify row by time in test

#945 Exports: order by plane when populating fluoro data

#942 SkinDose: try to calculate number of frames using exposure time / pulse width if number of frames not available

#941 Interface: the filtering submit button now updates chart options for fluoroscopy and mammography

#940 Installation: upgraded Django to 3.2, packages to latest versions

#937 Interface: correcting bottom row of exports table

#936 Tasks: added make_skin_map to background tasks on RF RDSR import

#935 SkinDose: fixed bug which applied 5x5 cm backscatter factor regardless of field size at skin

#934 DICOM Networking: QR queries are now logged and can be analysed through the web interface

#933 SkinDose: bug fixed that caused incorrect field size at skin for exposures with a non-zero secondary angle (caudocranial)

#931 Exports: export RF DAP as float instead of text

#928 Documentation: added restriction in postgres version for earlier OpenREM releases

#925 Docker: docs and config file for enabling bind mounts with SELinux

#923 Docker: docs and config for virtual directory install

#922 Database: optimise indexes and duplicate queries

#919 Interface: fixed bug preventing home page listing if study had no date

#917 Interface: added horizontal lines between chart option groups and shaded chart option CheckboxSelectMultiple items

#915 Interface and exports: expose mammo view modifier in interface and exports

#914 Imports: added compression force, pressure, contact area for mammo RDSR

#913 SkinDose: made 2d skin dose map overlay visible by default

#911 Charts: fixed issue with chart data sorting and added label wrap option

#910 SkinDose: fixed rendering of 2d skin dose map with head

#909 Code quality: all model imports absolute

#908 Imports: enabled Device Observer UID to be ignored for specific equipment models when creating display name data during RDSR import

#906 Charts: upgraded Plotly library to latest version

#905 Imports: fixed filter extraction code not working for Siemens Multix DX

#904 Testing: bumped Python image from 3.6 to 3.8

#903 Interface: added patient weight filters to the CT, RF and DX summary pages

#901 Charts: fixed issue where mammography mAs values were displayed 1000x too high on scatter plot

#897 Docker: fixed permissions for PixelMed - now using root throughout

#896 Imports: enabling import of DX with text string in PatientSize field

#893 Charts: fixed issue with over-time charts with many sub-plots failing to plot correctly

#892 Documentation: Removing references to native DICOM store and log

#888 SkinDose: added option to support skin dose calculations for listed systems only

#886 Code quality: addressed some SonarCloud issues

#882 SkinDose: added percentage of exposures that interact with phantom

#881 Charts: add option to remove multiple and trailing whitespace in category names

#880 Orthanc: added XA and RF to allowed modalities to enable physics QA images to be kept

#879 Charts: fixed sorting of fluoroscopy charts when split by physician

#877 Charts: added acquisition type restrictions to acquisition-level CT charts

#874 Documentation: updating DICOM query-retrieve documentation

#872 Charts: added ability to split fluoroscopy over-time and histogram charts by physician

#871 Charts: corrected RF chart x-axis labels

#870 Charts: reduced memory footprint of Pandas DataFrame creation

#869 Charts: added doc strings to new chart code

#868 Docker: fixed Python version at 3.8

#867 Documentation: updated chart documentation

#866 Simplified code for different character sets, enabled MultiValue SpecificCharacterSet

#865 Imports: enabled workaround to import Spectrum Dynamics RDSR

#864 Tasks: updated Celery settings for Celery 6.

#863 Interface: removed height and weight from CT study delete

#862 Interface: allow mapping of request, study and acquisition names to standard versions

#861 Interface: added ability to filter mammography on view code, compressed breast thickness and exposure control mode

#860 DICOM Networking: removed built-in DICOM Store SCP functionality

#858 DICOM Networking: query-retrieve logging, filtering and error handling improved

#857 Documentation: resolved documentation build errors

#856 Interface: removed CT acquisition type restriction tick boxes

#854 Interface: added date constraints to links on homepage

#853 Testing: reduced Bitbucket pipeline minutes usage

#852 Code quality: skin dose code formatted with Black

#850 Emails: added oldest study accession number to high fluoro dose alert email subject

#849 Docker: make docker URL an env variable

#847 Documentation: added copy button to commands, added prompts where appropriate

#845 Docker: moved Nginx config to bind folder

#844 Code quality: getting the pipelines right

#843 Code quality: closing target _blank phishing vulnerability

#842 Imports: ContextID code_meaning in make_skin_map and dxdetail

#841 Code quality: format code with Black

#840 Exports: added performing physician to fluoroscopy standard exports

#839 Documentation: removed redundant troubleshooting docs

#838 Imports: fixed issues with changed PersonName behaviour in pydicom 2.0

#836 Installation: updated requirements, Docker and docs for pynetdicom 1.5, pydicom 2.0

#835 Docker: fixed timeout issue with slow pagination

#830 Charts: fixed incorrect histogram data in charts

#829 Installation: added docs for Docker install on computer without internet access

#828 Docker: enabled email configuration to work

#827 SkinDose: made SkinDose results available in OpenREM and made alert triggering possible

#826 Code quality: split views to make more manageable and testable

#824 DICOM Networking: enabled declaration and testing of Orthanc Store SCP in Docker

#822 Code quality: removed remaining future references

#821 Code quality: fixed literal comparisons Docker was complaining about

#820 Documentation: converted changes to use sphinx-issues

#819 Removed colons from commands in documentation as they don’t format correctly in PDF

#818 Interface: refactored event number filtering

#817 SkinDose: fixed PEP8 and Codacy issues for skinDose

#816 Interface: fixed password change error

#815 Interface: fixed patient name filtering

#814 Deployment: automated deployment to dev.openrem.org and testing.openrem.org reintroduced

#812 DICOM Networking: separated abort from timeout in move request failure message

#808 Imports: caters for illegal use of mGy units in RDSR for dose at RP values

#807 Exports: fixed errors in PHE fluoro export when values are None

#805 DICOM Networking: fix errors on “association release” and “move complete”

#803 Fixed problem with multiple ModalitiesInStudy entries in c-find response

#800 Tasks: import and export tasks and DICOM queries and moves now listed with more information

#799 DICOM Networking: workaround for stationnames > 16 characters

#798 Exports: prevented error in export view if task_id is missing

#797 Exports: fixed string/byte issues with csv exports

#796 Exports: replaced file() with open() for Python 3.x compatibility

#795 Exports: included acquisition name in PHE radiographic projection export

#793 Installation: added Docker installation

#791 Exports: prevented error when trying to export DX data that has no filter information

#790 Python 3: remove basestring type

#789 Python 3: Median function aggregation code simplified; works with Python 3.7

#788 Tasks: Celery and RabbitMQ dropped, background task processing now managed within Python/OpenREM

#787 Interface: fixed login error

#786 Installation: increased Windows IIS timeouts in docs

#777 Updated OpenREM to use pydicom 1.3

#772 DICOM Networking: check for station name at series level or study, not both

#764 Imports: extractor functions log to extractor log instead of default

#744 Added overwrite mode to size import

#678 Enabled import of PX modality panoramic exam data - they appear in the Radiographic section

#664 Documentation: added sample config files to installation

#657 Documentation: split local_settings.py example into Windows and Linux versions

#530 Updated OpenREM to use pynetdicom 1.4

#513 Internationalization: first translation strings added to documentation

#512 Internationalization: first translation strings added to interface

#457 Updated OpenREM to use Django 2.2

#477 Charts: replaced HighCharts with open source Plotly library

#437 Updated OpenREM to use django-filters v2

#433 Import: Siemens Arcadis Varic dose reports are now imported

#404 Ported OpenREM to Python 3

#233 Charts: added charts of average CTDI and DLP over time

#94 Nuclear medicine: added nuclear medicine SPECT and PET functionality including RRSDR imports

0.10.0 (2019-11-08)¶

#785 Interface: added study level comments to rfdetail.html

#784 Imports: added laterality under target region as per 2017 CP 1676 change

#783 Interface: replaced static links by dynamic versions in rfdetail.html

#782 Exports: fixed RF export issue with studies containing multiple modalities

#781 Charts: fixed issue where charts were mis-labelled if “Case-insensitive categories” was unchecked

#780 Interface: changed mammography accumulated laterality to use code_value rather than code_meaning

#779 Installation: added restriction to django-qsstats-magic version

#778 Imports: added summary field population tests, fixed CT RDSR Total DLP import error

#776 Documentation: grammar and spelling correction for PHE exports

#775 Exports, documentation: fixed units issue and minor docs issue for PHE DX export

#774 Charts: fixed issue where charts were mis-labelled if “Plot a series per system” was unchecked

#771 Interface: entire fluoro exam row now highlighted when dose alert exceeded

#770 Imports: fix to allow non-conformant Opera Swing to import

#769 Interface: modified to allow detail view display of Ziehm studies with missing summary data

#768 Charts: study- and request-level charts now use study-level summary fields to improve performance

#765 Imports: updated error catching to allow Philips BigBore 4DCT RDSR to import

#763 Imports: corrected delta week fluoro study counting for dual plane modalities

#762 Interface: fixed error when deleting dual plane radiography studies

#761 Imports: fixed issue in high dose alert e-mail code where week_delta may be used before assignment

#759 Database: added study level summary fields and migration function

#758 Configuration: corrected issues with location of js_reverse static files

#750 Exports: added export tailored to the 2019 PHE DX dose survey

#746 Imports: enabled import of GE Elite Mini View C-arm RDSR with no template declaration

#181 Imports: corrected import of grid information from RDSRs

0.9.1 (2019-05-16)¶

#766 Documentation: updated the Windows Celery documentation to reflect changes required to shutdown Celery 3.1.25

#755 Interface: fix more static URLs to allow virtual directory web server configurations

#754 Documentation and install: updated docs and minimum version for collectstatic_js_reverse

#753 Query-retrieve: removed patient age fields from study level C-FIND that were not used

#752 Exports: fixed missing weight field in PHE CT 2019 export

#749 Documentation: updated the Linux quick install docs

#748 Charts: fixed error that caused blank charts if series per system was selected

#747 Installation: changed minimum Python version for current version of Flower

#743 Testing: added configuration to enable testing with default logging

#742 Interface: sorting of task tables now works in Internet Explorer 11

#740 Installation: fixed Celery version to avoid dependency on Django 1.11

#739 Imports: fixed import errors for GE surgical fluoroscopy

#738 Logging: added single_date query date to log, added tasks aborts to logs

#737 Interface and exports: specify number of events and export to PHE 2019 CT survey specification

#736 Query-retrieve: duplicate study level responses now removed from query

#735 Imports: switched to more secure defusedxml for parsing XML in comments

#734 Query-retrieve: handle illegal image level response with no instance number

#732 Query-retrieve: added advanced option to workaround empty series issue

#710 Interface: time-based columns in Celery and RabbitMQ tables now sorted correctly

#404 Code quality: changes to lead toward Python 3 compliance

0.9.0 (2019-03-06)¶

#733 Documentation: post-release fixes for 0.9.0 docs

#731 Imports: fixed another issue with display names on upgrade to 0.9

#729 Interface: replaced hard coded URLs in displaynameview.html and review_failed_imports.html with url names

#727 Imports: fixed issue with display names on upgrade to 0.9

#726 Documentation: updated to include the new task management function

#725 Charts: added fluoroscopy charts of DAP and frequency per requested procedure

#723 Task management: fixed issue with latest version of kombu and amqp on Windows

#722 Interface: dual-plane DX studies are now displayed without error in filtered list and study detail page

#721 Documentation: removed Django Debug Toolbar from default install and documented how to install and use it

#720 Interface: fixed small overlap between skin dose map and irradiation type table

#719 Interface: fixed hardcoded link in template rffiltered.html

#717 Query-retrieve: fixed problem where an error was thrown if association is None

#716 Task manager: removed assumption of queue name from RabbitMQ management interface

#714 Documentation: add missing documentation about changing STATIC_URL if serving OpenREM in a virtual directory

#711 Query-retrieve: fixed problem for zero image series when using -toshiba flag

#710 Interface: Celery and RabbitMQ tables can now be sorted by clicking on column headings

#709 Query-retrieve: corrected query logic for multiple modalities using #627 Modality tag at study level fix

#708 Query-retrieve: fixed problem for empty Series Number

#707 Interface: fixed issue where sigdig returned an error if it was passed an empty string

#706 Exports: fixed problem where filters were not respected for radiographic exports

#705 Task manager: added Flower to install and integrated to interface

#704 Imports: caters for illegal use of dGy.cm2 units in RDSR for DAP values

#703 Interface: fixed URL lookup error for failed imports on homepage

#702 Query-retrieve: fixed URLs in DICOM javascript files to allow virtual-directories

#701 Interface: made the fluoroscopy exposure detail table sortable by clicking on headers

#698 Imports: allow for incorrect case in Procedure reported tag in RDSR

#697 Testing: added tests for fluoroscopy high dose alerts (single-plane systems)

#696 Interface: fixed broken Delete Studies and Entry button

#695 Imports: added missing name attribute for size_abort url

#694 Query-retrieve: added extensive logging and summary to interface

#693 Interface: fixed display of numbers with significant places settings and comma localisation

#691 Interface: fixed URL lookup error for Display Names page

#690 Interface: added workload stats user option entry back into config menu

#689 Interface: fixed URL lookup error for DICOM summary page

#688 Interface: Add possibility to apply known display name based on Device Observer UID (default: disabled)

#685 Charts: fixed link code that would otherwise cause DLP per acquisition protocol chart histogram links to fail

#683 Installation: added VIRTUAL_DIRECTORY to the settings file to avoid updating local_settings file on upgrade

#682 Charts: fixed problem where links from histogram bars didn’t filter correctly when case-insensitive categories selected

#681 Imports: modified RDSR import to work with Varian RDSRs

#679 Interface: added ability to filter CT studies on acquisition type

#677 Interface: added additional filter materials to convert to abbreviations

#676 Imports: improved error handling on patient size imports

#675 Exports: improved resilience when export includes malformed studies

#674 Documentation: amended zip command in example Orthanc configuration to work with Linux and Windows

#673 Imports: handle empty NumericValues and workaround for incorrect Philips Azurion AcquisitionDeviceType

#672 Documentation: improve and extend linux one-page install

#670 Imports: handle illegal multi-value number in Toshiba RDSR with vHP

#668 Code quality: library import and blank space cleanup

#667 Web server: enable OpenREM to be hosted from a non-root folder/virtual-directory

#666 Query-retrieve: handle non-return of ModalitiesInStudy correctly

#665 Interface: added fluoroscopy high dose highlighting and e-mail alerts

#662 Administration: added facility to list and purge RabbitMQ queues

#659 Interface: made the latest study field in summary tables on the home page sort correctly

#658 Interface: added display of workload stats in home page modality tables

#637 Administration: added facility to list and purge RabbitMQ queues

#554 Query-retrieve: added time as matching argument for command line use

#461 Web server: enable OpenREM to be hosted from a non-root folder/virtual-directory (via #667)

#479 Administration: added facility to list and delete failed import studies

#349 Task management: fixed issue with Windows tasks not being killed on request

0.8.1 (2018-09-16)¶

#663 Interface: updated column headings on home page

#660 Documentation: corrected and improved Linux one-page install

#659 Interface: made the summary tables on the home page sortable by clicking on headers

#656 Install: pegged django-debug-toolbar to 1.9.1 until Django is upgraded

#654 Documentation: supplemented the Orthanc Lua file config option docs

#653 Docs: clarified notes to get link to Orthanc Lua file correct on release

#652 Documentation: added docs showing Celery daemonisation in Linux

#651 Documentation: added one-page full setup Ubuntu 18.04 install instructions

#650 Documentation: modified quick install virtualenv docs

#649 Documentation: instructions for updating hosts file for Ubuntu and RabbitMQ

#648 Documentation: clarified Toshiba options when not required

#647 Documentation: updated link to pixelmed

#646 Modified Celery import to avoid name clash in some circumstances

#645 Imports: prevent import failure when text is used in filter thickness field in DX image

#644 Exports: fixed error in exporting non-ASCII CT protocol acquisition names

#643 Installation: updated docs to make use of pip binaries for Postgres connector and numpy, Windows and Linux

#642 Skin dose maps: added catch for error when there are no events in the study

#641 Exports: mammography exports from filtered pages sorted by AGD no longer result in duplicate studies

#640 Exports: error in filter listing for NHSBSP csv exports corrected

#639 Charts: fixed problem where a blank category name may not be displayed correctly

#638 Skin dose maps: added a link to download data for stand-alone openSkin even when map displayed

#627 DICOM Networking: implemented workaround for query “bug” in Impax 6.6

#606 Interface: Made it possible for the user to change his/her password

0.8.0 (2018-06-11)¶

#635 Documentation: added Orthanc as preferred third party DICOM Store service

#634 Documentation: updated docs for import and query-retrieve duplicates processing

#633 Charts: fixed issue where charts failed if bar chart series name was null

#632 DICOM: move requests for queries that don’t exist now fail gracefully

#631 Skin dose maps: bug fixed that prevented message from displaying on screen when skin dose map cannot be calculated

#630 Documentation: improved installation instructions

#628 Imports: fixed code for importing when there are duplicate DX or MG studies in the database

#626 DICOM: isolated the generate modalities in study function and added testing

#625 Imports: now using event level UIDs to process continued, cumulative and duplicate RDSRs

#624 Charts: removed filter link on number of events histogram as it was not functioning correctly

#623 Imports: changed name of Toshiba image based extractor routine

#621 Documentation: reversed install order of openrem and pynetdicom due to new pydicom release

#619 Documentation: added workaround for outdated dictionary issues

#618 DICOM: fixed image level query that prevented RDSRs from being found

#617 Imports: fixed issue with multi study exams crashing the Toshiba extractor

#616 Documentation: added information for pip download -d

#615 Exports: added Target Exposure Index and Deviation Index to radiographic exports

#614 Exports: handle error when study is deleted during sheet creation for exports

#613 Imports: fixed dual modality type imports after ‘dual’ designation from ref #580

#612 Imports: prevented crash when RDSR was imported with AcquisitionProtocol sequence with no TextValue

#610 DICOM: query-retrieve changed to work for duplicate RDSRs, ref #114

#609 Interface: fixed the feature that toggles the selection when clicking anywhere on a display name table row

#608 Interface: fixed the broken sorting of display name table

#603 Interface: fixed JavaScript error if there are any None values in fluoro detail irradiation type table

#602 Skin dose maps: fixed error when there are multiple kVp values for a single irradiation event

#599 Installation: postgres instructions now include note about differing security choices

#597 Skin dose maps: documented that using a production webserver the default timeout value must be increased

#596 Documentation: added docs for using Gunicorn and NGINX on linux

#594 Display: corrected display of dual-plane DAP and RP dose in RF filtered view

#593 Imports: properly handles MultiValue filter material tags and permits aluminium spelling

#592 Documentation: added docs for using IIS on Windows

#589 Exports: now handles zero studies and studies deleted during exports sensibly

#587 Documentation: added instructions for Linux users to rotate logs

#586 Documentation: updated exports and detailed how pulse level data is exported

#585 Documentation: added information about multiple cumulative RDSRs

#584 Import, Interface, Export: RDSR with pulse level data now function

#583 Documentation: added information about dual mode modalities and deleting all from an X-ray unit

#582 Celery: updated results backend as amqp deprecated and slow

#581 Import scripts: interpreter line now always first, functions imported specifically

#580 Imports and Interface: one modality creating both DX and RF can now be handled appropriately

#579 Imports: dummy values for Toshiba CT import function now in settings.py, log file config in docs

#578 Exports: fixed NHSBSP export that was excluding RDSR imported Hologic studies

#575 Exports: export page now updates using AJAX and has a select all button

#573 Exports: corrected and clarified exposure time and duration units, added number of pulses

#572 Interface: homepage now populates as AJAX to increase responsiveness

#570 Charts: simplified chart function code

#569 Charts: fixed frequency issue with mean averages selected

#568 Imports: missing DICOM date-time no longer causes an error

#567 Celery: fixed dual-namespace imports of tasks

#566 Interface: correctly show “assumed patient mass” in case of set value of zero

#565 Interface: correctly handle dose area product with zero value

#564 Skin dose maps: text information on skin dose maps now embedded when saving the 2d or 3d map as a graphic

#562 Skin dose maps: error message on calculation failure now more explicit

#561 Imports: patient orientation modifier now correctly extracted from RDSR

#560 Exports: added study level comments

#559 Interface: date pickers inconsistent start day fixed

#558 Skin dose maps: set defaults instead of crashing if kV, dose, table or tube/detector position are missing

#557 Skin dose maps: improved construction of patient orientation code

#556 Exports: DX exports where TotalNumberOfRadiographicFrames is not populated now export

#552 Documentation: documented extractor for older Toshiba CT scanners

#551 Documentation: added procedure for opening csv files in Excel with non-ASCII characters

#550 Documentation: added a note to describe exposure time and duration for fluoroscopy studies

#549 Documentation: added procedure for fixing laterality on Hologic studies, ref #411

#547 Interface: improved handling of available time information for fluoro studies

#546 Query Retrieve: added flag and functionality to query for Toshiba images

#544 Interface: added procedure, requested procedure to summary listings and details and filtering

#543 Interface: added drop-down box to choose how many studies are displayed on filtered pages

#542 Interface: added display name to all detailed html pages

#541 Documentation: updated for celery on Windows

#540 Documentation: updated for current skinDose functionality

#539 Documentation: updated chart document to include series toggle buttons

#537 Charts: hide series function added

#536 Code quality: reduced javascript duplication and collected file groups into subfolders

#535 Interface: fixed problem where category names that included a plus symbol caused filtering and chart issues

#534 Interface: chart drilldown reported as not working - was actually due to a user’s database migrations

#533 Query Retrieve: Reduced number of simultaneous associations to one, reused for everything

#532 DICOM: documented how to work-around missing encoding charsets due to old pydicom

#529 Charts: added CT charts of number of irradiation events per study description and requested procedure

#528 Query Retrieve: reduced number of simultaneous associations to one, reused for everything

#526 Code quality: addressed some of the code quality/style issues raised by Codacy

#525 Importing: improved mammo import by checking compression force before converting to float

#524 Importing: improved mammo import by checking anode exists before converting to DICOM terms

#523 Importing: changed mammo import to use del_no_match instead of del_mg_im if not mammo

#522 Documentation: made it clearer on offline-install docs that version numbers will change

#521 Testing: added tests for dual source CT imports

#520 Imports: removed XML styling from Philips legacy CT comment creation

#519 Skin dose maps: fixed black on black text issue

#518 Importing: fixed imports where CT Target Region isn’t specified

#517 Interface: operator name is now displayed on the detail page for each modality, along with physician for CT and fluoro

#516 Imports: MultiValue person names are now stored as a decoded string, not a list

#511 Testing: develop and other branches can now be deployed to dev.openrem.org and testing.openrem.org automatically

#510 Imports: ‘not-patient-indicators’ can now be configured in the interface

#509 Skin dose maps: now recalculated on view if recorded height or weight has changed since last calculation

#508 Testing: DX sample files are now tested

#507 Interface: Mammo now filterable by study description, procedure, requested procedure and acquisition protocol

#506 Documentation: updated query-retrieve docs

#505 Charts: n is now displayed on charts

#504 Charts: Fixed issue with null values

#503 Internationalisation: more robust decoding and use of unicode throughout

#502 Testing: tests now work with SQLite3 and PostgreSQL databases

#501 Imports: Changed field type for CodeValue from 16 chars to text, allows for illegal long values

#500 Imports: Philips SC Dose Info with missing time stamps now import

#499 Imports: Now aborts gracefully with error log if no template in RDSR

#498 Exports: Missing units added to header fields

#497 Interface: Detailed fluoro study view: added irradiation type, pulse rate, dose to ref. point, secondary angle, total DAP and ref. point dose from each irradition type

#495 Charts: Reduced time taken to render scatter plots with multiple series

#494 Charts: Charts now ignore blank and zero-value data when calculating mean, median and number of events

#493 Charts: Added user option to made chart categories all lower case

#492 Exports: Each view is now unique for NHSBSP mammo exports as required by the NCCPM database

#491 Imports, Interface and Exports: CT Dose Check alerts and notifications are now extracted, displayed and exported

#490 Exports: Response object included for messages - removed as now asynchronous

#489 Exports: NHSBSP mammo exports deals with all views, excludes biopsies and specimens

#488 Exports: All exports now include study time

#487 Imports: CT RDSR now imports ‘procedure context’ correctly

#486 Imports: CT RDSR now imports ‘NameOfPhysiciansReadingStudy’ correctly

#485 Imports: CT RDSR now imports ‘target region’ correctly

#484 Exports and Interface: Exports and interface page views are now more efficient and (much) faster

#482 Imports: DX extractor now extracts acquisition protocol, requested procedure name and study name for Fuji Go mobile; extracts acquisition protocol for Toshiba Radrex equipment; extracts requested procedure name from Carestream DRX-Revolution mobiles

#480 Imports: Code and instructions to create and import an RDSR from Toshiba CT dose summary images and studies

#476 Imports: Mixed latin-1 and UTF8 characters now imported, but need to be handled better if possible

#475 Query Retrieve: Made -sr a stand-alone option - it has a very niche use-case!

#474 Logging: Changing to DEBUG logging level in local_settings.py will now be respected

#473 Query Retrieve: Added tests

#472 Query Retrieve: Overhauled the query retrieve routines

#471 Internationalisation: added configuration and docs to set the timezone

#470 Query Retrieve: Optimised CT filtering

#468 Query Retrieve: Station names can now be used for filtering if returned

#467 Testing: Added tests for mammography RDSR imports

#466 Query Retrieve: RDSR now retrieved in preference to images for MG and DX/CR

#465 Added newer SSDE and water equivalent diameter fields to database

#464 Imports: DX RDSR now imported properly

#463 Imports: Properly checks that Enhanced SR are GE dose reports before importing

#460 Interface: Display names table now sortable

#458 Exports: Filter thicknesses are rounded to max 4 significant figures on export

#454 Exports: Mean filter thickness now reported in exports

#453 Imports: DX with min filter thickness greater than max have values switched on import

#452 Exports: Added CTDIw phantom size to CT exports

#451 Skin dose maps: fixed issue with filters being referenced before being defined

#450 Imports: DX imports with filter thickness of 0.00 are now recorded as such

#449 Exports: Fixed a bug that prevented fluoro exports if protocol names had non-ASCII characters

#448 Documentation: Added a diagram showing the relationship between the OpenREM system components

#447 Imports: Modified rdsr and ctdetail template to import and display data from Pixelmed generated Toshiba RDSR

#446 Import: Extract additional Philips private information for Allura Xper systems, create workaround for missing end angles for rotational acquisitions

#445 Interface: Added function for user to determine between DX and fluoro for ambiguous modalities

#444 Imports: DX systems that submit RDSRs that look like fluoro can now be reclassified using #445

#443 Exports: Accession number and ID are now exported to XLSX as text. Thanks to @LuukO

#442 Exports: Fixed RF exports with multiple filters, added tests. Thanks to @LuukO

#441 Charts: Fixed a bug that broke chart links containing non-ASCII characters

#440 Charts: Fixed a bug in sorting.js so that undefined strings are handled correctly

#439 Charts: Added controls for plotting a series per system and calculation histogram data to each filtered view

#438 Skin dose maps: skin dose maps successfully calculated from existing studies; indication of assumed or extracted data shown

#434 Internationalisation: added passing char_set throughout the extractor functions (since largely made redundant again!)

#432 Imports: RDSR import function now looks in comment field for patient_table_relationship data

#431 Imports: fixed DX imports with MultiValue filter values (Cu+Al) again!

#430 Exports: fixed DX exports with multiple filters again, added tests

#429 Charts: added new mammo scatter plots. Thanks to @rijkhorst

#427 Testing: added a large number of tests that are automatically run on commit to bitbucket

#414 Reduced use of JavaScript global variables and improved JavaScript objects

#411 Imports: fixed laterality and accumulated AGD failure for Hologic DBT proprietary projection images

#323 Documentation: code autodocumentation largely now working again

#318 Database management: Display names view can be used to review and delete all studies from one source

#114 Imports: Subsequent RDSRs of the same study will now replace existing study in database

#61 Skin dose maps: These have been re-enabled, and currently work for Siemens systems

0.7.4 (2016-10-17)¶

#436 Install: temporary fix blocking django-filter latest version that breaks OpenREM

#431 Imports: fixed DX imports with MultiValue filter values (Cu+Al)

#430 Exports: fixed DX exports with multiple filters (Cu + Al)

0.7.3 (2016-08-30)¶

#426 Charts: added css so that wide chart data tables are displayed above the filter form div

#425 Exports: fixed error with non-ASCII characters being exported to csv

#424 Charts: fixed error where png or svg export of chart would show incorrect x-axis labels

#423 Charts: fixed error where some chart plotting options were not updated after being changed by the user

#422 Charts: added a button below each chart to toggle the display of the data table

#421 Charts: fixed error where only some scatter plot data was being exported to csv or xls files

#420 Charts: fixed error where frequency pie charts were only showing data from the first system

#419 Interface: fixed error where “Cancel” was ignored when deleting study in Firefox browser

#418 Exports: fixed error when exporting fluoroscopy study with missing xray_filter_material

#416 Charts: improved efficiency of JavaScript

#415 Database: migration for 0.6 upgraded installs to fix acquisition_device_type failures

#413 Documentation: removed erroneous reference to store queue in stop celery command

#410 Charts: fixed display of bar charts containing only one data point

#408 Charts: Increased number of items that can be shown on some Highcharts plots

#407 Fixed issue where skin dose map data was not being calculated on import

#406 Replaced Math.log10 JavaScript function with alternative function to fix IE11 skin dose map error

#405 Altered multi-line cell links in filtered pages so they work with IE8

0.7.1 (2016-06-10)¶

#403 Now deals with PersonName fields with latin-1 extended characters correctly

#402 Skin dose map data pickle files saved using gzip compression to save space

#401 Updated skin dose map documentation to say it won’t be in this release

#400 Strings are encoded as UTF-8 before being hashed to prevent errors with non-ASCII characters

#399 Migration file brought up to date for 0.6 to 0.7 upgrades

#398 Skin exposure maps are now stored in folders (feature postponed for future release)

#397 Skin exposure maps no longer available until orientation errors are fixed

#396 Charts: zooming on bar charts of average value vs. category now works

#395 Docs: offline Windows install instructions created, plus offline upgrade instructions

#394 Charts: made charts resize to fit containing div when browser is resized

#392 Charts: normalised histogram tooltip now correctly reports frequency

#391 Basic troubleshooting is now documented

#390 Charts: mammography and fluoroscopy charts added

#389 Charts: series without a name are now plotted under the name of Blank rather than not being plotted at all

#387 Added laterality to mammography exports

#385 Fixed issue with non-ASCII letters in RDSR sequence TextValue fields

#384 Fluoro exports for OpenSkin only consider copper filters now

#383 Refreshed settings.py to django 1.8 including updating template settings and TEMPLATE_CONTEXT_PROCESSORS

#380 Tube current now extracted from Siemens Intevo RDSR despite non-conformance

#379 Exposure time now populated for fluoro if not supplied by RDSR

#378 The display name of multiple systems can now be updated together using a single new name

#376 Corrected an ill-advised model change

#374 CTDIw phantom size now displayed in CT detail view

#373 Charts in some releases used GT rather than greater than or equal to for start date, now fixed

#372 Mammography studies now record an accumulated AGD per breast. Existing joint accumulated AGD values won’t be changed. Ordering by Accumulated AGD now creates an entry per accumulated AGD, one per breast

#371 Mammo RDSR generates average mA where not recorded, mammo image populates mA

#370 Added study description to mammography export

#369 Bi-plane fluoroscopy studies now export correctly

#368 Mammo RDSR now imports correctly

#365 Tube filtration is now displayed in the RF detail view

#364 Philips Allura fluorscopy RDSRs now import correctly

#362 Display of RF where bi-plane RDSRs have been imported no longer crash the interface

#360 Charts: saving data from average data charts as csv or xls now includes frequency values

#359 Added missing ‘y’ to query retrieve command line help

#358 Charts: chart sorting links and instructions now hidden when viewing histograms

#357 Charts: button to return from histogram now displays the name of the main chart

#356 Charts: histogram normalise button appears for all appropriate charts

#355 Charts: sorting now works as expected for plots with a series per system

#352 Fixed CT xlsx exports that had complete study data in each series protocol sheet (from earlier beta)

#351 Charts: simplified chart JavaScript and Python code

#350 DICOM networking documented for use with 3rd party store and advanced use with native

#348 Study delete confirmation page now displays total DAP for DX or CR radiographic studies

#346 Charts: exporting a chart as an image no longer requires an internet connection

#345 CSV size imports in cm are now stored as m in the database. Interface display of size corrected.

#343 Charts: user can now specify number of histogram bins in the range of 2 to 40

#342 Charts: improved the colours used for plotting chart data

#340 Fixed store failure to save due to illegal values in Philips private tags, improved exception code

#339 Improved extraction of requested procedure information for radiographic studies

#338 Fix Kodak illegally using comma in filter thickness values

#335 DICOM Store keep_alive and echo_scu functions now log correctly

#334 Fixed issue with tasks needing to be explicitly named

#333 Fixed StoreSCP not starting in beta 11 error

#332 Charts: some charts can now be plotted with a series per x-ray system

#331 Keep_alive tasks are now discarded if not executed, so don’t pile up

#329 All existing logging is now done via the same log files

#328 Store SCP no longer uses Celery tasks

#327 Celery workers now only take one task at a time

#325 Charts: switching charts off now leaves the user on the same page, rather than going to the home page

#324 Charts: forced chart tooltip background to be opaque to make reading the text easier

#320 The week now begins on Monday rather than Sunday on date form fields

#316 Query retrieve function can now exclude and include based on strings entered

#315 Charts: made size of exported chart graphics follow the browser window size

#314 One version number declaration now used for distribute, docs and interface

#313 Replaced non-working function with code to extract SeriesDescription etc in query response message

#312 Display names are now grouped by modality

#311 Queries are deleted from database after a successful C-Move

#310 Series level QR feedback now presented. Any further would require improvements in pynetdicom

#309 StoreSCP now deals safely with incoming files with additional transfer syntax tag

#308 Secondary capture images that don’t have the manufacturer field no longer crash the StoreSCP function

#306 Charts: added a button to each chart to toggle full-screen display

#305 Added links to documentation throughout the web interface

#304 Date of birth is now included in all exports that have either patient name or ID included

#303 Fixed a typo in 0.6.0 documents relating to the storescp command

#302 Improved handling of Philips Dose Info objects when series information sequence has UN value representation

#301 Charts: fixed bug that could stop average kVp and mAs radiographic plots from working

#300 Calling AE Title for Query Retrieve SCU is now configured not hardcoded

#299 Hash of MultiValued DICOM elements now works

#298 Added ordering by accumulated AGD for mammographic studies

#297 Fixed ordering by Total DAP for radiographic studies

#296 StoreSCP now logs an error message and continues if incoming file has problems

#295 Charts: fixed bug that arose on non-PostgreSQL databases

#294 Harmonised time display between filter list and detail view, both to HH:mm

#292 Added keep-alive and auto-start to DICOM stores

#291 Charts: fixed issue with CTDI and DLP not showing correct drilldown data

#290 Added new tables and fields to migration file, uses #288 and median code from #241

#289 Crispy forms added into the requires file

#288 Added device name hashes to migration file

#286 Increased granularity of permission groups

#285 Tidied up Options and Admin menus

#284 Fixed DICOM Query that looped if SCP respected ModalitiesInStudy

#282 Missing javascript file required for IE8 and below added

#281 Added check to import function to prevent extract failure

#280 Fixed typo in mammography export

#279 Charts: Fixed issue with median CTDI series from appearing

#278 Charts: Fixed javascript namespace pollution that caused links to fail

#277 Overhaul of acquisition level filters to get tooltip generated filters to follow through to export

#276 Unique fields cannot have unlimited length in MySQL - replaced with hash

#274 Charts: Fixed legend display issue

#273 Charts: Added plots of average kVp and mAs over time for DX

#272 Tweak to display of exam description for DX

#271 Fixed DX import failure where AcquisitionDate or AcquisitionTime are None

#270 Django 1.8 Admin site has a ‘view site’ link. Pointed it back to OpenREM

#268 Improved population of procedure_code_meaning for DX imports

#266 DICOM C-Store script added back in - largely redundant with web interface

#265 DICOM Store and Query Retrieve services documented

#263 Settings for keeping or deleting files once processed moved to database and web interface

#262 Dealt with issue where two exposures from the same study would race on import

#260 Fixed issue where import and export jobs would get stuck behind StoreSCP task in queue

#259 Link to manage users added to Admin menu

#258 Fixed DX import error where manufacturer or model name was not provided

#257 Documentation update

#256 Fixed errors with non-ASCII characters in imports and query-retrieve

#255 Charts: Small y-axis values on histograms are more visible when viewing full-screen

#254 Charts: Simplified chart data processing in the templates

#253 Charts: AJAX used to make pages responsive with large datasets when charts enabled

#252 Fixed duplicate entries in DX filtered data for studies with multiple exposures

#248 Charts: can now be ordered by frequency or alphabetically

#247 Fixed incorrect reference to manufacturer_model_name

#246 Charts: Added median data for PostgreSQL users

#245 Fixed error in csv DX export

#244 Fixed issue where scripts wouldn’t function after upgrade to Django 1.8

#243 Added distance related data to DX exports

#242 Distance source to patient now extracted from DX images

#241 Charts: Median values can be plotted for PostgreSQL users

#240 Charts: Improved DAP over time calculations

#239 Configurable equipment names to fix multiple sources with the same station name

#237 Charts: Tidied up plot data calculations in views.py

#235 Added patient sex to each of the exports

#234 Charts: Fixed error with datetime combine

#232 Charts: on or off displayed on the home page

#231 Charts: made links from requested procedure frequency plot respect the other filters

#230 Fixed error in OperatorsName field in DICOM extraction

#229 Charts: Added chart of DLP per requested procedure

#223 Charts: speed improvement for weekday charts

#217 Charts: Further code optimisation to speed up calculation time

#207 DICOM QR SCU now available from web interface

#206 DICOM Store SCP configuration now available from web interface

#183 Added options to store patient name and ID, and options to hash name, ID and accession number

#171 Root URL now resolves so /openrem is not necessary

#151 Suspected non-patient studies can now be filtered out

#135 GE Senographe DS now correctly records compression force in Newtons for new imports

#120 Improved testing of data existing for exports

#118 Upgraded to Django 1.8

#70 User is returned to the filtered view after deleting a study

#61 Skin dose maps for fluoroscopy systems can now be calculated and displayed

0.6.2 (2016-01-27)¶

#347 Django-filter v0.12 has minimum Django version of 1.8, fixed OpenREM 0.6.2 to max django-filter 0.11

#341 Changed references to the OpenSkin repository for 0.6 series.

0.6.1 (2015-10-30)¶

#303 Corrected name of Store SCP command in docs

0.6.0 (2015-05-14)¶

#227 Fixed import of RDSRs from Toshiba Cath Labs

#226 Charts: Updated Highcharts code and partially fixed issues with CTDIvol and DLP combined chart

#225 Charts: Added link from mAs and kVp histograms to associated data

#224 Charts: Added link from CTDIvol histograms to associated data

#221 Charts: Fixed issue where filters at acquisition event level were not adequately restricting the chart data

#219 Charts: Fixed issue where some charts showed data beyond the current filter

#217 Charts: Code optimised to speed up calculation time

#216 Fixed typo that prevented import of RSDR when DICOM store settings not present

#215 Charts: Fixed x-axis labels for mean dose over time charts

#214 Charts: Improved consistency of axis labels

#213 Fixed admin menu not working

#212 Charts: Created off-switch for charts

#210 OpenSkin exports documented

#209 Charts: Fixed server error when CT plots switched off and filter form submitted

#208 Charts: Fixed blank chart plotting options when clicking on histogram tooltip link

#205 Charts: Fixed issue of histogram tooltip links to data not working

#204 Charts: Fixed issue of not being able to export with the charts features added

#203 Charts: Fixed display of HTML in plots issue

#202 Charts: Added mean CTDIvol to charts

#200 Charts: Now exclude Philips Ingenuity SPRs from plots

#196 Added comments and entrance exposure data to DX export

#195 Fixed error with no users on fresh install

#194 Added more robust extraction of series description from DX

#193 Charts: Fixed reset of filters when moving between pages

#192 Created RF export for OpenSkin

#191 Charts: Factored out the javascript from the filtered.html files

#190 Charts: Added time period configuration to dose over time plots

#189 Charts: Fixed plotting of mean doses over time when frequency not plotted

#187 Charts: Merged the charts work into the main develop branch

#186 Fixed duplicate data in DX exports

#179 Charts: Added kVp and mAs plots for DX

#177 Charts: Fixed issue with date ranges for DX mean dose over time charts

#176 Charts: Added link to filtered dataset from mean dose over time charts

#175 Charts: Allowed configuration of the time period for mean dose trend charts to improve performance

#174 Charts: Fixed number of decimal places for mean DLP values

#173 Charts: Fixed plot of mean DLP over time y-axis issue

#170 Charts: Added plot of mean dose over time

#169 Charts: Improved chart colours

#157 Charts: Added chart showing number of studies per day of the week, then hour in the day

#156 Charts: Fixed issue with some protocols not being displayed

#155 Charts: Added chart showing relative frequency of protocols and study types

#140 Charts: Added configuration options

#139 Charts: Link to filtered dataset from histogram chart

#138 Charts: Number of datapoints displayed on tooltip

#135 Mammography compression force now only divides by 10 if model contains senograph ds Change in behaviour

#133 Documented installation of NumPy, initially for charts

#41 Preview of DICOM Store SCP now available

#20 Modality sections are now suppressed until populated

0.5.1 (2015-03-12)¶

#184 Documentation for 0.5.1

#180 Rename all reverse lookups as a result of #62

#178 Added documentation regarding backing up and restoring PostgreSQL OpenREM databases

#172 Revert all changes made to database so #62 could take place first

#165 Extract height and weight from DX, height from RDSR, all if available

#161 Views and exports now look for accumulated data in the right table after changes in #159 and #160

#160 Created the data migration to move all the DX accumulated data from TID 10004 to TID 10007

#159 Modified the DX import to populate TID 10007 rather than TID 10004. RDSR RF already populates both

#158 Demo website created by DJ Platten: http://demo.openrem.org/openrem

#154 Various decimal fields are defined with too few decimal places - all have now been extended.

#153 Changed home page and modality pages to have whole row clickable and highlighted

#150 DJ Platten has added Conquest configuration information

#137 Carestream DX multiple filter thickness values in a DS VR now extracted correctly

#113 Fixed and improved recording of grid information for mammo and DX and RDSR import routines

#62 Refactored all model names to be less than 39 characters and be in CamelCase to allow database migrations and to come into line with PEP 8 naming conventions for classes.

0.5.0 (2014-11-19)¶

Pull request from DJ Platten: Improved display of DX data and improved export of DX data

#132 Fixed mammo export error that slipped in before the first beta

#130 Only creates ExposureInuAs from Exposure if Exposure exists now

#128 Updated some non-core documentation that didn’t have the new local_settings.py reference or the new openremproject folder name

#127 DX IOD studies with image view populated failed to export due to lack of conversion to string

#126 Documentation created for the radiographic functionality

#125 Fixes issue where Hologic tomo projection objects were dropped as they have the same event time as the 2D element

#123 Fixed issue where filters came through on export as lists rather than strings on some installs

#122 Exports of RF data should now be more useful when exporting to xlsx. Will need refinement in the future

#26 Extractors created for radiographic DICOM images. Contributed by DJ Platten

#25 Views and templates added for radiographic exposures - either from RDSRs or from images - see #26. Contributed by DJ Platten

#9 Import of *.dcm should now be available from Windows and Linux alike

0.4.3 (2014-10-01)¶

#119 Fixed issue where Celery didn’t work on Windows. Django project folder is now called openremproject instead of openrem

#117 Added Windows line endings to patient size import logs

#113 Fixed units spelling error in patient size import logs

#112 File system errors during imports and exports are now handled properly with tasks listed in error states on the summary pages

#111 Added abort function to patient size imports and study exports

#110 Converted exports to use the FileField handling for storage and access, plus modified folder structure.

#109 Added example MEDIA_ROOT path for Windows to the install docs

#108 Documented ownership issues between the webserver and Celery

#107 Documented process for upgrading to 0.4.2 before 0.4.3 for versions 0.3.9 or earlier

#106 Added the duration of export time to the exports table. Also added template formatting tag to convert seconds to natural time

#105 Fixed bug in Philips CT import where decimal.Decimal was not imported before being used in the age calculation

#104 Added documentation for the additional study export functions as a result of using Celery tasks in task #19 as well as documentation for the code

#103 Added documentation for using the web import of patient size information as well as the new code

#102 Improved handling of attempts to process patient size files that have been deleted for when users go back in the browser after the process is finished

#101 Set the security of the new patient size imports to prevent users below admin level from using it

#100 Logging information for patient size imports was being written to the database - changed to write to file

#99 Method for importing remapp from scripts and for setting the DJANGO_SETTINGS_MODULE made more robust so that it should work out of the box on Windows, debian derivatives and virtualenvs

#98 Versions 0.4.0 to 0.4.2 had a settings.py.new file to avoid overwriting settings files on upgrades; renaming this file was missing from the installation documentation for new installs

#97 Changed the name of the export views file from ajaxviews as ajax wasn’t used in the end

#96 Changed mammo and fluoro filters to use named fields to avoid needing to use the full database path

#93 Set the security of the new exports to prevent users below export level from creating or downloading exports

#92 Add NHSBSP specific mammography csv export from Jonathan Cole - with Celery

#91 Added documentation for Celery and RabbitMQ

#90 Added delete function for exports

#89 Added the Exports navigation item to all templates, limited to export or admin users

#88 Converted fluoroscopy objects to using the Celery task manager after starting with CT for #19

#87 Converted mammography objects to using the Celery task manager after starting with CT for #19

#86 Digital Breast Tomosynthesis systems have a projections object that for Hologic contains required dosimetry information

#85 Fix for bug introduced in #75 where adaption of ptsize import for procedure import broke ptsize imports

#74 ‘Time since last study’ is now correct when daylight saving time kicks in

#39 Debug mode now defaults to False

#21 Height and weight data can now be imported through forms in the web interface

#19 Exports are now sent to a task manager instead of locking up the web interface

Reopened issue¶

#9 Issue tracking import using *.dcm style wildcards reopened as Windows cmd.exe shell doesn’t do wildcard expansion, so this will need to be handled by OpenREM in a future version

0.4.2 (2014-04-15)¶

#83 Fix for bug introduced in #73 that prevents the import scripts from working.

0.4.1 (2014-04-15)¶

#82 Added instructions for adding users to the release notes

0.4.0 (2014-04-15)¶

Note

#64 includes changes to the database schema and needs a user response - see version 0.4.0 release notes

#65 includes changes to the settings file which require settings information to be copied and files moved/renamed - see version 0.4.0 release notes

#80 Added docs for installing Apache with auto-start on Windows Server 2012. Contributed by JA Cole

#79 Updated README.rst instructions

#78 Moved upgrade documentation into the release notes page

#77 Removed docs builds from repository

#76 Fixed crash if exporting from development environment

#75 Fixed bug where requested procedure wasn’t being captured on one modality

#73 Made launch scripts and ptsizecsv2db more robust

#72 Moved the secret key into the local documentation and added instructions to change it to release notes and install instructions

#71 Added information about configuring users to the install documentation

#69 Added documentation about the new delete study function

#68 Now checks sequence code meaning and value exists before assigning them. Thanks to JA Cole

#67 Added ‘Contributing authors’ section of documentation

#66 Added ‘Release notes’ section of documentation, incuding this file

#65 Added new local_settings.py file for database settings and other local settings

#64 Fixed imports failing due to non-conforming strings that were too long

#63 The mammography import code stored the date of birth unnecessarily. Also now gets decimal_age from age field if necessary

#60 Removed extraneous colon from interface data field

#18 Studies can now be deleted from the web interface with the correct login

#16 Added user authentication with different levels of access

#9 Enable import of *.dcm

0.3.9 (2014-03-08)¶

Note

#51 includes changes to the database schema – make sure South is in use before upgrading. See https://docs.openrem.org/page/upgrade.html

#59 CSS stylesheet referenced particular fonts that are not in the distribution – references removed

#58 Export to xlsx more robust - limitation of 31 characters for sheet names now enforced

#57 Modified the docs slightly to include notice to convert to South before upgrading

#56 Corrected the mammography target and filter options added for issue #44

#53 Dates can now be selected from a date picker widget for filtering studies

#52 Split the date field into two so either, both or neither can be specified

#51 Remove import modifications from issue #28 and #43 now that exports are filtered in a better way after #48 and #49 changes.

#50 No longer necessary to apply a filter before exporting – docs changed to reflect this

#49 CSV exports changed to use the same filtering routine introduced for #48 to better handle missing attributes

#48 New feature – can now filter by patient age. Improved export to xlsx to better handle missing attributes

#47 Install was failing on pydicom – fixed upstream

0.3.8 (2014-03-05)¶

– File layout modified to conform to norms

#46 Updated documentation to reflect limited testing of mammo import on additional modalities

#45 mam.py was missing the licence header - fixed

#44 Added Tungsten, Silver and Aluminum to mammo target/filter strings to match – thanks to DJ Platten for strings

#43 Mammography and Philips CT import and export now more robust for images with missing information such as accession number and collimated field size

#42 Documentation updated to reflect #37

#37 Studies now sort by time and date

0.3.7 (2014-02-25)¶

#40 Restyled the filter section in the web interface and added a title to that section

#38 Column titles tidied up in Excel exports

#36 openrem_ptsizecsv output of log now depends on verbose flag

#35 Numbers no longer stored as text in Excel exports

0.3.6 (2014-02-24)¶

#34 Localised scripts that were on remote web servers in default Bootstrap code

#33 Documentation now exists for adding data via csv file

#24 Web interface has been upgraded to Bootstrap v3

#5 Web interface and export function now have some documentation with screenshots

0.3.5-rc2 (2014-02-17)¶

#32 Missing sys import bug prevented new patient size import from working

0.3.5 (2014-02-17)¶

– Prettified this document!

#31 Promoted patient size import from csv function to the scripts folder so it will install and can be called from the path

#30 Improved patient size import from csv to allow for arbitary column titles and study instance UID in addition to accession number.

#29 Corrected the docs URL in the readme

0.3.4-rc2 (2014-02-14)¶

#28 XLSX export crashed if any of the filter fields were missing. Now fills on import with ‘None’

#27 Use requested procedure description if requested procedure code description is missing

0.3.4 (2014-02-14)¶

– General improvements and addition of logo to docs

#23 Added Windows XP MySQL backup guide to docs

#22 Added running Conquest as a Windows XP service to docs

#15 Added version number and copyright information to xlsx exports

#14 Added version number to the web interface

#13 Improve the docs with respect to South database migrations

0.3.3-r2 (2014-02-04)¶

#12 Added this version history

#11 Documentation is no longer included in the tar.gz install file – see http://openrem.trfd.org instead

0.3.3 (2014-02-01)¶

Note

Installs of OpenREM earlier than 0.3.3 will break on upgrade if the scripts are called from other programs. For example openrem_rdsr is now called openrem_rdsr.py

– Added warning of upgrade breaking existing installs to docs

#10 Added .py suffix to the scripts to allow them to be executed on Windows (thanks to DJ Platten)

#8 Removed superfluous ‘/’ in base html file, harmless on linux, prevented Windows loading stylesheets (thanks to DJ Platten)

#7 Added windows and linux path examples for test SQLite database creation

#6 Corrected renaming of example files installation instruction (thanks to DJ Platten)

#4 Added some text to the documentation relating to importing files to OpenREM

#3 Corrected copyright notice in documentation

0.3.2 (2014-01-29)¶

Initial version uploaded to bitbucket.org

Release notes and upgrade instructions¶

Each release comes with specific upgrade instructions, so please follow the links below for the appropriate version.

Version specific information¶

This release:

OpenREM Release Notes version 1.0.0¶

Headline changes¶

Python 3
Django 2.2
Docker or direct install on Windows and Linux
Celery, Flower and RabbitMQ removed from requirements
Performing physician added to standard fluoroscopy exports (#840)
Station name checked at series level only, option to check at study level only instead (#772)

Upgrade from 0.10.0¶

Review the Installation doc to find the upgrade options

Upgrade from an older version¶

Upgrade to OpenREM 0.10.0 from 0.7.3 or later

Previous releases:

OpenREM Release Notes version 0.10.0¶

Headline changes¶

Database: new summary fields introduced to improve the responsiveness of the interface - requires additional migration step
Imports: enabled import of GE Elite Mini View C-arm, Opera Swing R/F and Philips BigBore CT RDSRs that have issues
Imports: updated event level laterality to import from new location after DICOM standard change proposal CP1676
Interface: highlight row when dose alert exceeded
Exports: added fluoroscopy and radiography exports tailored for UK PHE dose survey
General: Lots of fixes to imports, interface, charts etc

Upgrade to current version¶

Upgrade to OpenREM 0.10.0 from 0.7.3 or later and then upgrade to 1.0.

Original upgrade instructions¶

For the original upgrade instructions, the last docs release to include them was 0.10.0-docs

OpenREM Release Notes version 0.9.1¶

Headline changes¶

Imports: fixed imports for GE surgical flat panel c-arm with irregular value types and value meanings
Interface: added feature to filter by specific number of exposure types – CT only
Query-retrieve: new option to get SR series when PACS returns empty series level response
Query-retrieve: handle illegal missing instance number in image level response
Query-retrieve: improved logging
Exports: added export to UK PHE 2019 CT survey format
General documentation and interface improvements, bug fixes, and changes to prepare for Python 3

Upgrade to current version¶

Upgrade to OpenREM 0.10.0 from 0.7.3 or later and then upgrade to 1.0.

Original upgrade instructions¶

For the original upgrade instructions, the last docs release to include them was 0.10.0-docs

OpenREM Release Notes version 0.9.0¶

Headline changes¶

Interface: added feature to display workload stats in the home page modality tables
Interface: added Fluroscopy high dose alerts feature
Interface: dual-plane DX studies can now be handled in summary list and study detail pages
Interface: new option to set display name when unique fields change based on device observer UID in RDSR
Charts: added fluoroscopy charts of DAP and frequency per requested procedure, fixed bugs in links for others
Query-retrieve: handle non-return of ModalitiesInStudy correctly
Query-retrieve: increased query logging and summary feedback
Query-retrieve: use time range in search (command line only)
Imports: fix for empty NumericValues in RDSR
Imports: fix for Toshiba RDSR with incorrect multiple values in SD field for vHP
Imports: fix for Philips Azurion RDSR with incorrect AcquisitionDeviceType
Imports: fix for Varian RDSRs
Exports: made more robust for exporting malformed studies, fixed filtering bugs
Administration: automatic e-mail alerts sent when fluoroscopy studies exceed a dose alert level
Administration: added facility to list and delete studies where the import failed
Administration: added interface to RabbitMQ queues and Celery tasks
Administration: short-term fix for task performance and control on Windows
Documentation: further refinement of the linux one-page install
Installation: Running the OpenREM website in a virtual directory

Upgrade to current version¶

Upgrade to OpenREM 0.10.0 from 0.7.3 or later and then upgrade to 1.0.

Original upgrade instructions¶

For the original upgrade instructions, the last docs release to include them was 0.10.0-docs

OpenREM Release Notes version 0.8.1¶

Headline changes¶

Documentation: improved docs and added one-page complete install on Ubuntu instructions
Install: temporary fix for dependency error
Interface: added feature to allow users to change their own password
Charts: fixed problem where a blank category name may not be displayed correctly
Imports: reduced list of scanners that work with the legacy Toshiba CT extractor
Imports: improved handling of non-conformant DX images with text in filter thickness fields
Query-Retrieve: added non-standard option to work-around bug in Impax C-FIND SCP
Exports: fixed bug in mammography NHSBSP exports that incorrectly reported the filter material in some circumstances
Exports: fixed bug where sorting by AGD would cause duplicate entries for bilateral studies
Exports: fixed another non-ASCII bug

Upgrade to current version¶

Upgrade to OpenREM 0.10.0 from 0.7.3 or later and then upgrade to 1.0.

Specific upgrade instructions¶

For the original upgrade instructions, the last docs release to include them was 0.10.0-docs

OpenREM Release Notes version 0.8.0¶

Headline changes¶

This release has extensive automated testing for large parts of the codebase (for the first time)
Code quality is much improved, reduced duplication, better documentation, many bugs fixed
Imports: RDSR from a wider range of systems now import properly
Imports: Better distinction and control over defining RDSR studies as RF or DX
Imports: Code and instructions to generate and import RDSR from older Toshiba CT scanners
Imports: DICOM Query-Retrieve functionality has been overhauled
Imports: Duplicate checking improved to allow cumulative and continued study RDSRs to import properly
Imports: indicators that a study is not a patient can now be configured in the web interface
Imports, display and export: Better handling of non-ASCII characters
Interface: More detailed, consistent and faster rendering of the data in the web interface
Interface: Maps of fluoroscopy radiation exposure incident on a phantom (Siemens RDSRs only)
Interface: More and better charts, including scatter plots for mammography
Interface: Display names dialogue has been extended to allow administration of all studies from each source
Exports: Much faster, and more consistent
Documentation: Extensive user documentation improvements

Upgrade to current version¶

Upgrade to OpenREM 0.10.0 from 0.7.3 or later and then upgrade to 1.0.

Specific upgrade instructions¶

For the original upgrade instructions, the last docs release to include them was 0.10.0-docs

OpenREM Release Notes version 0.7.4¶

Headline changes¶

Imports: DX images now import with multiple filters that are MultiValue as well as comma separated
Exports: DX data now correctly exports to csv and xlsx if studies include multiple filters (eg Cu+Al)
Install: New release of dependency django-filter breaks OpenREM. Pegged at previous version for now

Upgrade to current version¶

Upgrade to OpenREM 0.10.0 from 0.7.3 or later and then upgrade to 1.0.

Specific upgrade instructions¶

For the original upgrade instructions, the last docs release to include them was 0.10.0-docs

OpenREM Release Notes version 0.7.3¶

Headline changes¶

Database: New migration file for upgrades from 0.6 series databases
Charts: Fixed display and export errors, improved layout and increased the number of data points that can be plotted
Interface: Fixed multi-line cells in tables so that the links work in IE8
Interface: Fixed delete cancel button in firefox
Exports: Fixed export of non-ASCII characters to csv file

Upgrade to current version¶

Upgrade to OpenREM 0.10.0 from 0.7.3 or later and then upgrade to 1.0.

Specific upgrade instructions¶

For the original upgrade instructions, the last docs release to include them was 0.10.0-docs

OpenREM Release Notes version 0.7.1¶

Headline changes¶

System
- Django upgraded to version 1.8
- Median function added to the database if using PostgreSQL
- New user-defined display name for each unique system so that rooms with the same DICOM station name are displayed separately
- Patient name and ID can optionally be stored in system, available for searching and export, but not displayed
- Patient name, ID and accession number can be stored as a one-way hash, and remain searchable
- Permission system has become more granular
- System can now accept non-ASCII characters in protocol names etc
- Menus have been tidied up
- Settings file has been updated
Charts and interface
- Bar chart data points sorted by frequency, value or name in ascending or descending order
- CT chart of DLP per requested procedure type
- CT chart of requested procedure frequency
- CT chart of CTDIvol per study description
- Chart data returned using AJAX to make pages more responsive
- Chart plotting options available via Config menu
- Charts can now be made full-screen
- CTDIw phantom size is displayed with the CTDIvol measurement on the CT study detail page
- Charts show a series called “Blank” when the series name is None
- Queries for chart data now faster in most situations
- Histograms can be disabled or enabled for bar charts
- User-specified number of histogram bins from 2 to 40
- Mammography chart of average glandular dose vs. compressed thickness
- Mammography chart showing the number of studies carried out per weekday
- Fluoroscopy chart of average DAP for each study description
- Fluoroscopy chart of the frequency of each study description
- Fluoroscopy chart showing the number of studies carried out per weekday
- Context specific documentation has been added to the Docs menu
DICOM Networking
- Query retrieve function is now built in to query PACS systems or modalities via the Import menu
- Configuring and running DICOM Store SCP is available and managed in the web interface, but not recommended
- Documentation improved
Imports
- Mammography RDSRs import correctly
- Mammography imports from images now create an accumulated AGD value per breast
- GE Senographe DS compression now recorded correctly in Newtons for new imports
- Philips Allura fluoroscopy RDSRs import correctly, including calculating the exposure time
- Bi-plane fluoroscopy imports can now be displayed in the web interface
- Patient height imports from csv now convert from cm to m - previously height was assumed to be cm and inserted into database without change. Existing height data will remain as cm value for csv imports, and m value for RDSR imports
- Better handling of non-ASCII characters
- Tube current is now extracted from Siemens Intevo RDSRs
Exports
- Patient sex is included in all exports
- Filters generated by navigating through charts can now be used to filter export data
- Study description and laterality are now included in mammography exports
- Bi-fluoroscopy studies can be exported
Skin dose maps
- Skin dose maps have been withdrawn from OpenREM version 0.7.0 due to incorrect orientation calculations that need to be fixed before openSkin can be reimplemented into OpenREM

Changes since 0.7.0¶

Extremely minor change to the documenation links

Specific upgrade instructions¶

For the original upgrade instructions, the last docs release to include them was 0.10.0-docs

OpenREM Release Notes version 0.6.0¶

Headline changes¶

Charts
Preview of DICOM Store SCP functionality
Exports available to import into openSkin
Modalities with no data are hidden in the user interface
Mammography import compression force behaviour changed
Import of Toshiba planar RDSRs fixed

Changes for 0.6.2¶

Minor update due prevent new installs from installing a non-compatible version of django-filter. The link to openSkin has also been updated in the fluoroscopy detail page.

There is no advantage to updating to this version over 0.6.0

Release 0.6.1 was just a documentation only change to update the link to openSkin.

Specific upgrade instructions¶

For the original upgrade instructions, the last docs release to include them was 0.10.0-docs

Summary of new features¶

Charts¶

Release 0.6.0 has a range of charting options available for CT and radiographic data. These charts allow visualisation of trends and frequencies to inform surveys and monitor performance. For more information, please see Charts.

DICOM Store Service Class Provider¶

OpenREM can now act as the DICOM Store service, allowing direct sending of DICOM objects from modalities to OpenREM without needing to use Conquest or any other DICOM Store SCP. This feature is a preview as it hasn’t been extensively tested, but it is expected to work. For more information, please see Direct from modalities.

Exports for openSkin¶

Fluoroscopy studies can now be exported in a format suitable for importing into Jonathan Cole’s openSkin software. The export link is on the fluoroscopy study detail page. The software for creating the exposure incidence map can be downloaded from https://bitbucket.org/openskin/openskin/downloads (choose the zip file), and information about the project can be found on the openSkin wiki. The software allows the user to choose between a 2D phantom that would represent the dose to a film laying on the couch surface, or a simple 3D phantom made up of a cuboid and two semi-cylinders (these can be seen on the Phantom design section of the wiki). For both options the output is an image of the dose distribution in 2D, along with calculated peak skin dose information.

Automatic hiding of unused modality types¶

A fresh install of OpenREM will no longer show any of the four modality types in the tables or in the navigation bar at the top. As DICOM objects are ingested, the appropriate tables and navigation links are created.

Therefore a site that has no mammography for example will no longer have that table or navigation link in their interface.

Mammography import compression force change¶

Prior to version 0.6, the compression force extracted from the mammography image header was divided by ten before being stored in the database. This was because the primary author only had access to GE Senograph DS units, which store the compression force in dN, despite claiming using Newtons in the DICOM conformance statement.

The code now checks for the term senograph ds contained in the model name. If it matches, then the value is divided by ten. Otherwise, the value is stored without any further change. We know that later GE units, the GE Senograph Essential for example, and other manufacturer’s units store this value in N. If you have a case that acts like the Senograph DS, please let us know and we’ll try and cater for that.

If you have existing non-GE Senograph mammography data in your database, the compression force field for those studies is likely to be incorrect by a factor of ten (it will be too small). Studies imported after the upgrade will be correct. If this is a problem for you, please let us know and we’ll see about writing a script to correct the existing data.

Import of Toshiba Planar RDSRs fixed¶

Toshiba include Patient Orientation and Patient Orientation Modifier information in their cath lab RDSRs. The extractor code was deficient for this as the RDSRs previously used didn’t have this information. This has now been fixed. There might however be an issue with Station Name not being provided - it is not yet clear if this is a configuration issue.

OpenREM Release Notes version 0.5.1¶

Headline changes¶

Major database modification to remove table name length errors
Extended the field value lengths to better incorporate all possible values and decimal places
Improved import of grid and filter information from DX images
Improved DX summary and detail web pages
Any item in a row can now be clicked to move between the home and filtered pages

Specific upgrade instructions¶

For the original upgrade instructions, the last docs release to include them was 0.10.0-docs

OpenREM Release Notes version 0.5.0¶

Headline changes¶

Import, display and export of CR/DX data from image headers
Export of study data from fluoroscopy to xlsx files
Importing data from Windows using *.dcm style wildcards
Hologic tomography projection images are no longer excluded if part of a Combo exposure

Specific upgrade instructions¶

For the original upgrade instructions, the last docs release to include them was 0.10.0-docs

OpenREM Release Notes version 0.4.3¶

Headline changes¶

Export of study information is now handled by a task queue - no more export time-outs.
Patient size information in csv files can now be uploaded and imported via a web interface.
Proprietary projection image object created by Hologic tomography units can now be interrogated for details of the tomosynthesis exam.
Settings.py now ships with its proper name, this will overwrite important local settings if upgrade is from 0.3.9 or earlier.
Time since last study is no longer wrong just because of daylight saving time!
Django release set to 1.6; OpenREM isn’t ready for Django 1.7 yet
The inner openrem Django project folder is now called openremproject to avoid import conflicts with Celery on Windows
DEBUG mode now defaults to False

Specific upgrade instructions¶

For the original upgrade instructions, the last docs release to include them was 0.10.0-docs

OpenREM Release Notes version 0.4.2¶

Headline changes¶

This release fixes a major bug introduced in 0.4.0 regarding the import scripts.

Specific upgrade instructions¶

Upgrading from 0.3.9 or earlier¶

Follow the instructions in OpenREM Release Notes version 0.4.0

Upgrading from 0.4.0 or above¶

Move straight to version 0.4.3 and follow the instructions in OpenREM Release Notes version 0.4.3

OpenREM Release Notes version 0.4.1¶

Headline changes¶

This release is exacly the same as 0.4.1 bar some documentation corrections

Specific upgrade instructions¶

Please use the 0.4.0 release notes for upgrades from 0.3.9

OpenREM Release Notes version 0.4.0

OpenREM Release Notes version 0.4.0¶

Headline changes¶

User authentication has been added
Studies can be deleted from the web interface
Import scripts can now be passed a list of files, eg python openrem_rdsr.py *.dcm
Date of birth no longer retained for mammography (bug fix - correct behaviour already existed for other imports)
General bug fixes to enable import from wider range of sources
Improved user documentation

Specific upgrade instructions¶

For the original upgrade instructions, the last docs release to include them was 0.10.0-docs

Contributing authors¶

Many people have contributed to OpenREM - either with code, documentation, translations, bugs, examples or ideas, including:

BjörnAlbers
Erlend Andersen
Njål Brekke
Elly Castellano
Jonathan Cole
Jamie Dormand
Ben Earner
Louise Giansante Martins
Daniel Gordon
Hamid Khosravi
Laurence King
Wens Kong
Eivind Larsen
John Loveland
Paolo Marcheschi
Ed McDonagh
Richard Miles
Allan Nordhøy
Luuk Oostveen
David Platten
Richard Raynor
Erik-Jan Rijkhorst
Kevin Schärer - Kantonsspital Aarau AG
Arnold Schilham
Marcelo Sosa
Georg Stamm
Jannis Widmer - Kantonsspital Aarau AG
Tim de Wit
Daniel Wyatt

Special thanks go to Gerd Lutters of Kantonsspital Aarau AG for providing ideas and and civil service computer science students to work on OpenREM (Jannis and Kevin).

Diagram of system components¶

$digraph { node [fixedsize=true width=2.0 height=0.75 fontsize=10 margin="0.0,0.0"]; // Define the things on the server subgraph cluster_server { label="OpenREM server"; tooltip="The server"; fontname="Helvetica-bold"; fontsize=16; style=filled; color=lightgrey; node [style=filled color=white]; // Define the nodes for the data storage, display and retrieval webserver [label="Web server¹" fontname="Helvetica" tooltip="Serve web pages to the user" shape="box"]; python_django [label="OpenREM\nDjango app" fontname="Helvetica" tooltip="Python web framework" shape="box"]; database [label="Database³" fontname="Helvetica" tooltip="Relational database management system" shape="parallelogram"]; skin_dose_map_data [label="Skin dose map\ndata calculation,\nstorage, retrieval" fontname="Helvetica" tooltip="Calculate, store and retrieve skin dose map data" shape="parallelogram"]; server_media_folder [label="Server file storage\n(Media Home folder)" fontname="Helvetica" tooltip="File storage on the server" shape="parallelogram"]; data_export [label="Data export to\nlocal file system" fontname="Helvetica" tooltip="Files are made available to the user via a web page URL" shape="box"]; // Define the links between the data storage, display and retrieval webserver -> python_django [dir=both]; python_django -> database [dir=both label="via psycopg2\nPython adapter" fontsize=8 fontname="Courier"]; skin_dose_map_data -> server_media_folder [dir=both]; skin_dose_map_data -> python_django [style=dotted dir=both]; data_export -> server_media_folder; data_export -> python_django [style=dotted dir=both]; // Define the nodes for the DICOM store, database population and skin dose map calculation conquest [label="DICOM StoreSCP²" fontname="Helvetica" tooltip="Conquest, acting as a DICOM storage SCP" shape="box"]; conquest_script [shape=diamond label="Does the\nobject contain\nuseful data?" fontname="Helvetica" tooltip="Process the rules in dicom.ini"]; populate_database [label="Extract information from\nthe DICOM object to the\nOpenREM database" fontname="Helvetica", tooltip="Extract data using OpenREM's python scripts" shape="box"]; delete_object [label="Delete the DICOM object\nfrom the DICOM store" fontname="Helvetica" tooltip="Delete the DICOM object from the local store SCP" shape="box"]; calc_skin_dose_map [shape=diamond label="Calculate\nskin dose\nmap?" fontname="Helvetica" tooltip="Calculate the skin dose map?"]; blank_node_1 [shape=none style=invisible]; // Define the links between the DICOM store, database population and skin dose map calculation conquest_script -> populate_database [label="Yes" fontcolor=darkgreen fontsize=8 fontname="Courier"]; populate_database -> delete_object; conquest_script -> delete_object [label="No" fontcolor=red fontsize=8 fontname="Courier"]; conquest -> conquest_script; populate_database -> calc_skin_dose_map; // Define the links between the two groups // populate_database -> database python_django -> populate_database [dir=back] calc_skin_dose_map -> skin_dose_map_data [style=dotted label="Yes" fontcolor=darkgreen fontsize=8 fontname="Courier"] // Force certain nodes to be on the same level so that the diagram looks good (hopefully) {rank=same; webserver conquest}; {rank=same; python_django->blank_node_1->delete_object->populate_database [style=invis]; rankdir=LR;} } // Define the web browser, modality and pacs nodes web_browser [label="Client\nweb browser" fontname="Helvetica" tooltip="The user's web browser" shape="box" style=rounded]; modality [label="X-ray imaging\nmodality" fontname="Helvetica" tooltip="Data send from an x-ray imaging modality" shape="parallelogram"]; pacs [label="PACS" fontname="Helvetica" tooltip="A Picture Archiving and Communication System" shape="parallelogram"]; blank_node_2 [label="" shape=none]; // Define the links that the browser, modality and pacs have web_browser -> webserver [dir=both label="via user-requests\land Ajax\l" fontsize=8 fontname="Courier" tooltip="Ajax used to retrieve chart data"]; modality -> conquest [label="via modality\lconfiguration\l" fontsize=8 fontname="Courier"]; pacs -> conquest [label="via OpenREM\lquery-retrieve\l" fontsize=8 fontname="Courier"]; // pacs -> python_django [style=dotted dir=both]; // Force the web browser, blank node, modality and pacs to be on the same level in a specific order {rank=same; web_browser->blank_node_2->modality->pacs [style=invis]; rankdir=LR;}; }$

Alternatives¶

1: Web servers¶

The recommended web server for Windows is Microsoft IIS - see to be written docs for details. This has replaced the recommendation to use Apache due to difficulties in obtaining the required binary files, as described in the Advanced server configuration section of the installation document.

The recommended web server for Linux is Gunicorn with NGINX - see Webserver for details.

Alternatively, a built-in web server is included that will suffice for testing purposes and getting started.

2: DICOM Store node¶

Any DICOM Store can be used, as long as it can be used to call the OpenREM import script. See DICOM Network Configuration for more details. Orthanc is the recommended DICOM Store services to use; it is installed by default in Docker, see DICOM Store SCP for Linux installation, to be written for Windows installation, and the DICOM Store section for configuration help.

3: Database¶

PostgreSQL is the recommended database to use with OpenREM. It is the only database that OpenREM will calculate median values for charts with. Other databases can be used with varying capabilities; see the Django documentation for more details. For testing only, the built-in SQLite3 database can be used, but this is not suitable for later migration to a production database.