Skip to main content

Data Consistency for Magnetic Resonance Imaging

Project description

Data Consistency for Magnetic Resonance Imaging

CodeQL codecov Tox Code style: black


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:

The following models are implemented for accelerated MRI 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).

Quantitative Imaging:

The following models are implemented for quantitative imaging: 1.quantitative Cascades of Independently Recurrent Inference Machines (qCIRIM), 2.quantitative End-to-End Variational Network (qE2EVN), 3.quantitative Independently Recurrent Inference Machines (qIRIM), 4.quantitative Recurrent Inference Machines (qRIM).

Note: Currently only the above models are implemented. More models can be added by extending the reconstruction models for quantitative imaging. If you wish to extend the toolbox, please open an issue.

Segmentation:

Coming soon...

Usage

Check the projects page for more information of how to use mridc.

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

API Documentation

Documentation Status

Access the API Documentation here

License

License: Apache 2.0

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.

Citation

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

@misc{mridc,
    author = {Karkalousos Dimitrios, Zhang Chaoping, and Caan Matthan},
    title = {MRIDC: Data Consistency for Magnetic Resonance Imaging},
    year = {2022},
    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’

[2] Zhang, C. et al. (2022) 'A unified model for reconstruction and R2* mapping of accelerated 7T data using the quantitative Recurrent Inference Machine'

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

mridc-0.2.0.tar.gz (254.7 kB view details)

Uploaded Source

Built Distribution

mridc-0.2.0-py3-none-any.whl (345.1 kB view details)

Uploaded Python 3

File details

Details for the file mridc-0.2.0.tar.gz.

File metadata

  • Download URL: mridc-0.2.0.tar.gz
  • Upload date:
  • Size: 254.7 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.8.13

File hashes

Hashes for mridc-0.2.0.tar.gz
Algorithm Hash digest
SHA256 fc2dc63902723e12ac1f87d053131593529150877a8711d466de8e06cfbd4daa
MD5 b7382bacd6f4f37cbe5ade640749b8d2
BLAKE2b-256 4caf730c05f07e5c4b2900541814d8da06e86b30340785ddd9a62e24d624643e

See more details on using hashes here.

Provenance

File details

Details for the file mridc-0.2.0-py3-none-any.whl.

File metadata

  • Download URL: mridc-0.2.0-py3-none-any.whl
  • Upload date:
  • Size: 345.1 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.8.13

File hashes

Hashes for mridc-0.2.0-py3-none-any.whl
Algorithm Hash digest
SHA256 bd99ff0294ce74f9f23a502d5c58e01c2b80f150aaba3baa5cd7dd0890ad33f8
MD5 8e7a5172a20929966abe45fb0027f715
BLAKE2b-256 fa7cbc55dfec2d2426b9a0f8cec5431117532bc6fc864d38b70c788641a6affd

See more details on using hashes here.

Provenance

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page