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clinical2advanced

Enhancing tumour microstructural quantification with machine learning and diffusion-relaxation MRI. Journal of Magnetic Resonance Imaging.

Journal Link | Cite

clinical2advanced is a straight-forward method for densly-sampled diffusion-relaxation MR images prediction from routine diffusion-only images. It also provides steps for advanced quantitative diffusion-relaxation metrics prediction from simple ADC estimations, despite the first approach is recommended.

Content

  • run_me.py: code to run the pipeline. Write run_me.py -h or run_me.py --help in your promt to print the help manual
  • options.py: class defining the mandatory and optional inputs
  • data_loader.py: contains the functions to load and preprocess the data
  • utils.py: class containing multiple utils functions used troughtout the pipeline
  • patcher.py: contains to functions to patch and unpatch the data if required by the user
  • train_test.py: contains the functions to train and/or test the different algorithms
  • myNet.py: class of multi-layer network.

Approaches

This repository allows the prediction step of both approaches defined in Macarro C. et al. 2024. The fitting steps are not included here.

  • maps-to-maps: diffusion-relaxation advanced quantitative parametric maps are predicted from simpler ADC measurements
  • signal-to-signal: diffusion-relaxation signals are predicted from simpler diffusion-only signals

clinical2advanced_approaches

Dependencies

The code has been written in python v3.8.12 and the following packages are required:

  • numpy v1.20.3
  • nibabel v3.2.1
  • torch v1.11
  • scikit-learn v1.0.2
  • joblib v1.1.0

Reference

If you find our work useful, please consider citing our paper:

Macarro, C., Bernatowicz, K., Garcia-Ruiz, A. et al. (2024), Enhancing Tumor Microstructural Quantification With Machine Learning and Diffusion-Relaxation MRI. J Magn Reson Imaging. https://doi.org/10.1002/jmri.29484

License

This repository is distributed under the Attribution-NonCommercial-ShareAlike 4.0 International license (CC BY-NC-SA 4.0). Copyright (c) 2024, Fundació Privada Institut d’Investigació Oncològica de Vall d’Hebron (Vall d'Hebron Institute of Oncology (VHIO), Barcelona, Spain). All rights reserved. See licence here: LICENSE.txt