xinpeiwangmri / fastmri-reproducible-benchmark

Try several methods for MRI reconstruction on the fastmri dataset.

fastMRI reproducible benchmark

The idea of this repository is to have a way to rapidly benchmark new solutions against existing reconstruction algorithms on the fastMRI dataset single-coil track. The reconstruction algorithms implemented or adapted to the fastMRI dataset include to this day:

• zero filled reconstruction
• LORAKS, using the LORAKS Matlab toolbox
• Wavelet-based reconstruction (i.e. solving an L1-based analysis formulation optimisation problem with greedy FISTA), using pysap-mri
• U-net
• DeepCascade net
• KIKI net
• Learned Primal Dual, adapted to MRI reconstruction

All the neural networks are implemented in TensorFlow with the Keras API.

How to train the neural networks

The scripts to train the neural networks are located in fastmri_recon/training_scripts/. You just need to install the package and its dependencies:

pip install . &&\
pip install -r requirements.txt

How to write a new neural network for reconstruction

The simplest and most versatile way to write a neural network for reconstruction is to subclass the CrossDomainNet class. An example is the PDnet

Data requirements

fastMRI

The fastMRI data must be located in a directory whose path is stored in the FASTMRI_DATA_DIR environment variable. It can be downloaded on the official website after submitting a request (bottom of the page).

The package currently supports public single coil and multi coil knee data.

OASIS

The OASIS data must be located in a directory whose path is stored in the OASIS_DATA_DIR environment variable. It can be downloaded on the XNAT store after creating an account. The project is OASIS3.

Citation

This work will be presented at the International Symposium on Biomedical Imaging (ISBI) in April 2020. An extended version has been published in MDPI Applied sciences. If you use this package or parts of it, please cite one of the following work:

• Benchmarking Deep Nets MRI Reconstruction Models on the FastMRI Publicly Available Dataset
• Benchmarking MRI Reconstruction Neural Networks on Large Public Datasets