New open-source, platform independent python based library for model-driven registration in quantitative renal MRI.
Currently developed for and tested on the iBEAt study dataset. Can be utilised for other quantitative MRI studies.
Models included: T1, T2, T2*, DTI, DWI, DCE-MRI. To be extended for: MT, PC-MRI, ASL.
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Have
Python >= 3.6installed and typepip install mdr-library. If you are a developer, then you can runpip install -r requirements.txtin a terminal or in an IDE of your preference. This will install the Python Packages required to run this library, with special focus on ITK-Elastix which is the one used for the model-driven registration. -
Download test DICOM data, unzip file and place it in the 'tests/test_data' folder.
Run MDR_test_main.py in the tests folder. Read the code in the other python files in the tests folder to find examples of how to use the MDR package.
The MDR library has been developed to simplify use, reduce workflow overhead, and allow generalisability by restructuring the MDR algorithm into an open-source, platform-independent, python based library for model based registration in quantitative renal MRI.
The prototype MDR algorithm (Tagkalakis F, et al. Model-based motion correction outperforms a model-free method in quantitative renal MRI. Abstract-1383, ISMRM 2021) was initially developed using Elastix for co-registration and validated for renal T1-mapping, DTI and DCE against groupwise model-free registration (GMFR). The prototype MDR algorithm has been restructured and extended into the MDR-Library to provide a simple and intuitive application programming interface using ITK-Elastix.
For more details about the code, please consult the Reference Manual
Updating the Reference Manual:
First, run the command pdoc --html --force --output-dir "docs" "MDR" in the parent folder.
Then, move all files in "docs/MDR" to "docs" folder and delete the "MDR" folder. Alternatively, you can type the following in the terminal:
mv docs/MDR/* docs
rmdir docs/MDR
Finally, commit the newly generated files to Github so that the website is refreshed.
The iBEAt study is part of the BEAt-DKD project. The BEAt-DKD project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 115974. This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA with JDRF. For a full list of BEAt-DKD partners, see www.beat-dkd.eu.
For queries please email: kanishka.sharma@sheffield.ac.uk or s.sourbron@sheffield.ac.uk