A large-scale dataset of both raw MRI measurements and clinical MRI images
Switch branches/tags
Nothing to show
Clone or download
Latest commit e9b97be Nov 27, 2018
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
common Initial Commit Nov 26, 2018
data Initial Commit Nov 26, 2018
models Initial Commit Nov 26, 2018
.gitignore Initial Commit Nov 26, 2018
CODE_OF_CONDUCT.md Initial Commit Nov 26, 2018
CONTRIBUTING.md Initial Commit Nov 26, 2018
LICENSE.md Initial Commit Nov 26, 2018
README.md Fixed Bibtex entry Nov 28, 2018
requirements.txt Initial Commit Nov 26, 2018

README.md

fastMRI

Accelerating Magnetic Resonance Imaging (MRI) by acquiring fewer measurements has the potential to reduce medical costs, minimize stress to patients and make MR imaging possible in applications where it is currently prohibitively slow or expensive.

fastMRI is collaborative research project from Facebook AI Research (FAIR) and NYU Langone Health to investigate the use of AI to make MRI scans faster. NYU Langone Health has released fully anonymized Knee MRI datasets that can be downloaded from the fastMRI dataset page.

This repository contains convenient PyTorch data loaders, subsampling functions, evaluation metrics, and reference implementations of simple baseline methods.

Citing

If you use the fastMRI data or this code in your research, please consider citing the fastMRI dataset paper:

@inproceedings{zbontar2018fastMRI,
  title={fastMRI: An Open Dataset and Benchmarks for Accelerated MRI},
  author={Jure Zbontar and Florian Knoll and Anuroop Sriram and Matthew J. Muckley and Mary Bruno and Aaron Defazio and Marc Parente and Krzysztof J. Geras and Joe Katsnelson and Hersh Chandarana and Zizhao Zhang and Michal Drozdzal and Adriana Romero and Michael Rabbat and Pascal Vincent and James Pinkerton and Duo Wang and Nafissa Yakubova and Erich Owens and C. Lawrence Zitnick and Michael P. Recht and Daniel K. Sodickson and Yvonne W. Lui},
  journal = {ArXiv e-prints},
  archivePrefix = "arXiv",
  eprint = {1811.08839},
  year={2018}
}

Dependencies

We have tested this code using:

  • Ubuntu 18.04
  • Python 3.6
  • CUDA 9.0
  • CUDNN 7.0

You can find the full list of Python packages needed to run the code in the requirements.txt file. These can be installed using:

pip install -r requirements.txt

Directory Structure & Usage

  • common: Contains several utility functions and classes that can be used to create subsampling masks, evaluate results and create submission files.
  • data: Contains PyTorch data loaders for loading the fastMRI data and PyTorch data transforms useful for working with MRI data. See data/README.md for more information about using the data loaders.
  • models: Contains the baseline models.

Testing

Run pytest.

Submitting to Leaderboard

Run your model on the provided test data and create a zip file containing your predictions. Upload this to any publicly accessible cloud storage (e.g. Amazon S3, Dropbox etc) and then create a JSON file with your information.

The common/utils.py file has some convenience functions to help with this:

  • save_reconstructions function saves the data in the correct format.
  • create_submission_file creates a JSON file for submission.

Submit the JSON file on the EvalAI page.

License

fastMRI is MIT licensed, as found in the LICENSE file.