test-hazen 1.0.3

An automatic MRI QA tool

hazen

Quality assurance framework for Magnetic Resonance Imaging
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Overview

hazen is a software framework for performing automated analysis of magnetic resonance imaging (MRI) Quality Assurance data.

It provides automatic quantitative analysis for the following measurements of MRI phantom data:

• Signal-to-noise ratio (SNR)
• Spatial resolution
• Slice position and width
• Uniformity
• Ghosting
• MR Relaxometry

Some example outputs from hazen:

hazen snr	hazen ghosting

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Installation

pip

Hazen can be installed using pip on python 3.9 or higher, it is recommended to use a virtual environment

python3 -m venv hazen-venv
source hazen-venv/bin/activate
pip install hazen

Docker

The Docker version of hazen as it is easy to get up-and-running and is linked to the most stable release. Refer to the Docker installation instructions to install Docker on your host computer.

For ease of use, it is recommended to copy the hazen-app script to a location accessible on the path such as /usr/local/bin. This will allow you to run hazen from any location on your computer. Then, to use Docker hazen, simply run the hazen-app script appended with the function you want to use (e.g.: snr).

In Terminal:

cd hazen
cp ./hazen-app /usr/local/bin

# run hazen with CLI arguments
hazen-app snr tests/data/snr/Siemens/

latest: Pulling from gsttmriphysics/hazen
Digest: sha256:18603e40b45f3af4bf45f07559a08a7833af92a6efe21cb7306f758e8eeab24a
Status: Image is up to date for gsttmriphysics/hazen:latest
docker.io/gsttmriphysics/hazen:latest
{   'snr_smoothing_measured_seFoV250_2meas_slice5mm_tra_repeat_PSN_noDC_2_1': 191.16,
    'snr_smoothing_measured_seFoV250_2meas_slice5mm_tra_repeat_PSN_noDC_3_1': 195.58,
    'snr_smoothing_normalised_seFoV250_2meas_slice5mm_tra_repeat_PSN_noDC_2_1': 1866.09,
    'snr_smoothing_normalised_seFoV250_2meas_slice5mm_tra_repeat_PSN_noDC_3_1': 1909.2,
    'snr_subtraction_measured_seFoV250_2meas_slice5mm_tra_repeat_PSN_noDC_2_1': 220.73,
    'snr_subtraction_normalised_seFoV250_2meas_slice5mm_tra_repeat_PSN_noDC_2_1': 2154.69}

Linux & MacOS

For developers, hazen can be installed from source using pip. We highly recommend using a virtual environment.

# Install OpenSSL
# Go to local hazen repo directory
cd hazen

# Create and activate a virtual environment
python3 -m venv ./hazen-venv
source hazen-venv/bin/activate

# Install requirements
pip install --upgrade pip setuptools wheel
pip install -r requirements.txt

# Install hazen
python setup.py install

# Run tests to ensure everything is working
pytest tests/

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Usage

Command Line

The CLI version of hazen is designed to be pointed at single folders containing DICOM file(s). Example datasets are provided in the tests/data/ directory. If you are not using the Docker version of hazen, replace hazen-app with hazen in the following commands.

To perform an SNR measurement on the provided example Philips DICOMs:

hazen-app snr tests/data/snr/Philips

To perform a spatial resolution measurement on example data provided by the East Kent Trust:

hazen-app spatial_resolution tests/data/resolution/philips

To see the full list of available tools, enter:

hazen-app -h

The --report option provides additional information for some of the functions. For example, the user can gain additional insight into the performance of the snr function by entering:

hazen-app snr tests/data/snr/Philips --report

Web Interface

WIP: we are developing a web interface for hazen.

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Contributing

• The Release Manager should create a release branch for the future planned release e.g. release-X.X.X
• The RMs shall organise backlog refinement sessions to ensure issues are allocated to the appropriate release
• The RM should ensure their release branch is kept up-to-date with master
• PRs should be merged into the appropriate release branch for the issue(s) it is addressing

If you want to contribute to the development of hazen, please take a look at: CONTRIBUTING.md.

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Users

Please raise an Issue if you have any problems installing or running hazen.

We have used hazen with MRI data from a handful of different MRI scanners, including multiple different vendors. If your MRI data doesn't work with hazen, or the results are unexpected, please submit an Issue and we will investigate.

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Releasing

The Release Manager should ensure:

• All outstanding issues for the current release have been closed, or, transferred to future release.

• All tests are passing on Github Actions.

• All documentation has been updated with correct version numbers:

  • Version number in docs/conf.py
  • Version number in hazenlib/__init__.py
  • Version number in CITATION.cff

• The release branch has been merged into main branch

• A new release has been created with a new version tag (tag = version number)

• RMs of other branches should update their release from the latest release as soon as possible and deal with any merge conflicts.

• RMs: Tom Roberts, Lucrezia Cester