Data Consistency for Magnetic Resonance Imaging

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Introduction

MRIDC is a toolbox for applying AI methods on MR imaging. A collection of tools for data consistency and data quality is provided for MRI data analysis. Primarily it focuses on the following tasks:

Reconstruction:

1.Cascades of Independently Recurrent Inference Machines (CIRIM), 2.Compressed Sensing (CS), 3.Convolutional Recurrent Neural Networks (CRNN), 4.Deep Cascade of Convolutional Neural Networks (CCNN), 5.Down-Up Net (DUNET), 6.End-to-End Variational Network (E2EVN), 7.Joint Deep Model-Based MR Image and Coil Sensitivity Reconstruction Network (Joint-ICNet), 8.Independently Recurrent Inference Machines (IRIM), 9.KIKI-Net, 10.Learned Primal-Dual Net (LPDNet), 11.MultiDomainNet, 12.Recurrent Inference Machines (RIM), 13.Recurrent Variational Network (RVN), 14.UNet, 15.Variable Splitting Network (VSNet), 16.XPDNet, 17.and Zero-Filled reconstruction (ZF).

Segmentation:

Coming soon...

Acknowledgements

MRIDC is based on the NeMo framework, using PyTorch Lightning for feasible high-performance multi-GPU/multi-node mixed-precision training.

For the reconstruction methods:

• the implementations of 6 and 14 are thanks to and based on the fastMRI repo.
• The implementations of 7, 9, 10, 11, 13, and 16 are thanks to and based on the DIRECT repo.

Installation

MRIDC is best to be installed in a Conda environment.

conda create -n mridc python=3.9
conda activate mridc

Pip

Use pip installation if you want the latest stable version.

pip install mridc

From source

Use source installation if you want the latest development version, as well as for contributing to MRIDC.

git clone https://github.com/wdika/mridc
cd mridc
./reinstall.sh

Datasets

Recommended public datasets to use with this repo:

• fastMRI,
• Fully Sampled Knees.

API Documentation

Access the API Documentation here

License

Citation

Please cite MRIDC using the "Cite this repository" button or as

@misc{mridc,
    author = {Karkalousos, Dimitrios and Caan, Matthan},
    title = {MRIDC: Data Consistency for Magnetic Resonance Imaging},
    year = {2021},
    url = {https://github.com/wdika/mridc},
}

Papers

The following papers use the MRIDC repo:

[1] Karkalousos, D. et al. (2021) ‘Assessment of Data Consistency through Cascades of Independently Recurrent Inference Machines for fast and robust accelerated MRI reconstruction’