We introduce and demonstrate a new paradigm for quantitative parameter mapping in MRI. Parameter mapping techniques, such as diffusion MRI and quantitative MRI, have the potential to robustly and repeatably measure biologically-relevant tissue maps that strongly relate to underlying microstructure. Quantitative maps are calculated by fitting a model to multiple images, e.g. with least-squares or deep learning. However, the overwhelming majority of model fitting techniques assume that each voxel is independent, ignoring any co-dependencies in the data. This makes model fitting sensitive to voxelwise measurement noise, hampering reliability and repeatability. We propose a self-supervised deep variational approach that breaks the assumption of independent pixels, leveraging redundancies in the data to effectively perform data-driven regularisation of quantitative maps. We demonstrate that our approach outperforms current model fitting techniques in dMRI simulations and real data. Especially with a Gaussian mixture prior, our model enables sharper quantitative maps, revealing finer anatomical details that are not presented in the baselines. Our approach can hence support the clinical adoption of parameter mapping methods such as dMRI and qMRI.
[To do] Refactorizing the code for the real MRI data.
conda env create -f requirements.yaml
conda activate deep_mri- Apply for access first: https://www.humanconnectome.org/study/hcp-young-adult/data-use-terms
- After your access request is granted:
cd ..
mkdir data
cd data
gdown --folder https://drive.google.com/drive/folders/1HulbCuPAm4SltZN_iP83W8StaSQoirX3?usp=sharing
gdown --folder https://drive.google.com/drive/folders/1h7I8-ezSMMGNovkGsD2Ou7c6MI2tBypD?usp=sharingpython train_simulated.py --config 'configs/baseline_simulated.yaml' 
The visual results of the proposed methods (VAE-UniG and VAE-GMM) vs the non-dl and self-supervised baseline. Our methods can discover more anatomical structures as highlighted in the zoomed in areas.
