The quantitative MRI network (qMRINet) toolbox enables voxel-by-voxel fitting of quantitative MRI (qMRI) models and resampling of qMRI protocols with fully-connected deep neural networks.
To use qMRINet you need a Python 3 distribution such as Anaconda. Additionally, you need the following third party modules/packages:
Getting qMRINet is extremely easy: cloning this repository is all you need to do. The tools would be ready for you to run.
If you use Linux or MacOS:
- Open a terminal;
- Navigate to your destination folder;
- Clone qMRINet:
$ git clone https://github.com/fragrussu/qMRINet.git
- qMRINet is ready for you in
./qMRINet. qMRINet tools are available here:./qMRINet/tools; a user guide and some tutorials are available here:./qMRINet/tutorials. - You should now be able to use the code. Try to print the manual of a tool in your terminal, for instance of
syndata_deepqmri.py, to make sure this is really the case:
$ python ./qMRINet/tools/syndata_deepqmri.py --help
qMRINet is based on 3 different python classes: qmrisig, qmripar and qmriinterp. These are defined in the deepqmri.py file and described in detail in this user guide. The guide contains:
- information on qMRINet classes and on their methods, including illustrations;
- a list of signal models currently implemented in qMRINet.
If you use qMRINet, please remember to cite our work:
"Deep learning model fitting for diffusion-relaxometry: a comparative study"; Grussu F, Battiston M, Palombo M, Schneider T, Gandini Wheeler-Kingshott CAM and Alexander DC. Proceedings of the 2020 International MICCAI Workshop on Computational Diffusion MRI, 2021, pp 159-172, doi: 10.1007/978-3-030-73018-5_13.
"Feasibility of data-driven, model-free quantitative MRI protocol design: application to brain and prostate diffusion-relaxation imaging". Grussu F, Blumberg SB, Battiston M, Kakkar LS, Lin H, Ianuș A, Schneider T, Singh S, Bourne R, Punwani S, Atkinson D, Gandini Wheeler-Kingshott CAM, Panagiotaki E, Mertzanidou T and Alexander DC. Frontiers in Physics 2021, 9:752208, doi: 10.3389/fphy.2021.752208.
Earlier preliminary results are available in this preprint: "Deep learning model fitting for diffusion-relaxometry: a comparative study"; Grussu F, Battiston M, Palombo M, Schneider T, Gandini Wheeler-Kingshott CAM and Alexander DC. biorxiv 2020, doi: 10.1101/2020.10.20.347625.
Disclosure: FG was supported by PREdICT, a study co-funded by AstraZeneca (Spain). TS is an employee of DeepSpin (Germany) and previously worked for Philips (United Kingdom). MB is an employee of ASG Superconductors. AstraZeneca, Philips, DeepSpin and ASG Superconductors were not involved in the study design; collection, analysis, interpretation of data; manuscript writing and decision to submit the manuscript for publication; or any other aspect concerning this work.
qMRINet is distributed under the BSD 2-Clause License, Copyright (c) 2020 University College London. All rights reserved (license).
The use of qMRINet MUST also comply with the individual licenses of all of its dependencies.
The development of qMRINet was funded by the Engineering and Physical Sciences Research Council (EPSRC EP/R006032/1, M020533/1, EP/N018702/1 G007748, I027084) and by the UK Research and Innovation (UKRI) programme (MR/T020296/1, funding M.P.). This project has received funding under the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 634541, and from: Rosetrees Trust (UK, funding F.G.); Spinal Research (UK), Wings for Life (Austria), Craig H. Neilsen Foundation (USA) for INSPIRED; Wings for Life (169111); UK Multiple Sclerosis Society (grants 892/08 and 77/2017); the Department of Health’s National Institute for Health Research (NIHR) Biomedical Research Centres and UCLH NIHR Biomedical Research Centre.