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A large-scale dataset of both raw MRI measurements and clinical MRI images.

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Website | Dataset | GitHub | Publications

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 a 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 and brain MRI datasets that can be downloaded from the fastMRI dataset page. Publications associated with the fastMRI project can be found at the end of this README.

This repository contains convenient PyTorch data loaders, subsampling functions, evaluation metrics, and reference implementations of simple baseline methods. It also contains implementations for methods in some of the publications of the fastMRI project.


The fastMRI Dataset

There are multiple publications describing different subcomponents of the data (e.g., brain vs. knee) and associated baselines. All of the fastMRI data can be downloaded from the fastMRI dataset page.

Code Repository

For code documentation, most functions and classes have accompanying docstrings that you can access via the help function in IPython. For example:

from import SliceDataset


Dependencies and Installation

Note: Contributions to the code are continuously tested via GitHub actions. If you encounter an issue, the best first thing to do is to try to match the tests environment in setup.cfg, e.g., pip install --editable ".[tests]" when installing from source.

Note: As documented in Issue 215, there is currently a memory leak when using h5py installed from pip and converting to a torch.Tensor. To avoid the leak, you need to use h5py with a version of HDF5 before 1.12.1. As of February 16, 2022, the conda version of h5py 3.6.0 used HDF5 1.10.6, which avoids the leak.

First install PyTorch according to the directions at the PyTorch Website for your operating system and CUDA setup. Then, run

pip install fastmri

pip will handle all package dependencies. After this you should be able to run most of the code in the repository.

Installing Directly from Source

If you want to install directly from the GitHub source, clone the repository, navigate to the fastmri root directory and run

pip install -e .

Package Structure & Usage

The repository is centered around the fastmri module. The following breaks down the basic structure:

fastmri: Contains a number of basic tools for complex number math, coil combinations, etc.

  • Contains data utility functions from original data folder that can be used to create sampling masks and submission files.
  • fastmri.models: Contains reconstruction models, such as the U-Net and VarNet.
  • fastmri.pl_modules: PyTorch Lightning modules for data loading, training, and logging.

Examples and Reproducibility

The fastmri_examples and banding_removal folders include code for reproducibility. The baseline models were used in the arXiv paper.

A brief summary of implementions based on papers with links to code follows. For completeness we also mention work on active acquisition, which is hosted in another repository.


Run pytest tests. By default integration tests that use the fastMRI data are skipped. If you would like to run these tests, set SKIP_INTEGRATIONS to False in the conftest.

Training a model

The data README has a bare-bones example for how to load data and incorporate data transforms. This jupyter notebook contains a simple tutorial explaining how to get started working with the data.

Please look at this U-Net demo script for an example of how to train a model using the PyTorch Lightning framework.

Submitting to the Leaderboard

NOTICE: As documented in Discussion 293, the domain was transferred from Meta ownership to NYU ownership on 2023-04-17, and NYU has not yet rebuilt the site. Until the site and leaderbaords are rebuilt by NYU, leaderboards will be unavailable. Mitigations are presented in Discussion 293.


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


If you use the fastMRI data or code in your project, please cite the arXiv paper:

    title={{fastMRI}: An Open Dataset and Benchmarks for Accelerated {MRI}},
    author={Jure Zbontar and Florian Knoll and Anuroop Sriram and Tullie Murrell and Zhengnan Huang and Matthew J. Muckley and Aaron Defazio and Ruben Stern and Patricia Johnson and Mary Bruno 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 Nafissa Yakubova and James Pinkerton and Duo Wang 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},

If you use the fastMRI prostate data or code in your project, please cite that paper:

  title={{FastMRI Prostate}: A Publicly Available, Biparametric {MRI} Dataset to Advance Machine Learning for Prostate Cancer Imaging},
  author={Tibrewala, Radhika and Dutt, Tarun and Tong, Angela and Ginocchio, Luke and Keerthivasan, Mahesh B and Baete, Steven H and Chopra, Sumit and Lui, Yvonne W and Sodickson, Daniel K and Chandarana, Hersh and Johnson, Patricia M},
  journal = {ArXiv e-prints},
  archivePrefix = "arXiv",

List of Papers

The following lists titles of papers from the fastMRI project. The corresponding abstracts, as well as links to preprints and code can be found here.

  1. Zbontar, J.*, Knoll, F.*, Sriram, A.*, Murrell, T., Huang, Z., Muckley, M. J., ... & Lui, Y. W. (2018). fastMRI: An Open Dataset and Benchmarks for Accelerated MRI. arXiv preprint arXiv:1811.08839.
  2. Zhang, Z., Romero, A., Muckley, M. J., Vincent, P., Yang, L., & Drozdzal, M. (2019). Reducing uncertainty in undersampled MRI reconstruction with active acquisition. In CVPR, pages 2049-2058.
  3. Defazio, A. (2019). Offset Sampling Improves Deep Learning based Accelerated MRI Reconstructions by Exploiting Symmetry. arXiv preprint, arXiv:1912.01101.
  4. Knoll, F.*, Zbontar, J.*, Sriram, A., Muckley, M. J., Bruno, M., Defazio, A., ... & Lui, Y. W. (2020). fastMRI: A Publicly Available Raw k-Space and DICOM Dataset of Knee Images for Accelerated MR Image Reconstruction Using Machine Learning. Radiology: Artificial Intelligence, 2(1), page e190007.
  5. Knoll, F.*, Murrell, T.*, Sriram, A.*, Yakubova, N., Zbontar, J., Rabbat, M., ... & Recht, M. P. (2020). Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challenge. Magnetic Resonance in Medicine, 84(6), pages 3054-3070.
  6. Sriram, A., Zbontar, J., Murrell, T., Zitnick, C. L., Defazio, A., & Sodickson, D. K. (2020). GrappaNet: Combining parallel imaging with deep learning for multi-coil MRI reconstruction. In CVPR, pages 14315-14322.
  7. Recht, M. P., Zbontar, J., Sodickson, D. K., Knoll, F., Yakubova, N., Sriram, A., ... & Zitnick, C. L. (2020). Using Deep Learning to Accelerate Knee MRI at 3T: Results of an Interchangeability Study. American Journal of Roentgenology, 215(6), pages 1421-1429.
  8. Pineda, L., Basu, S., Romero, A., Calandra, R., & Drozdzal, M. (2020). Active MR k-space Sampling with Reinforcement Learning. In MICCAI, pages 23-33.
  9. Sriram, A.*, Zbontar, J.*, Murrell, T., Defazio, A., Zitnick, C. L., Yakubova, N., ... & Johnson, P. (2020). End-to-End Variational Networks for Accelerated MRI Reconstruction. In MICCAI, pages 64-73.
  10. Defazio, A., Murrell, T., & Recht, M. P. (2020). MRI Banding Removal via Adversarial Training. In Advances in Neural Information Processing Systems, 33, pages 7660-7670.
  11. Muckley, M. J.*, Riemenschneider, B.*, Radmanesh, A., Kim, S., Jeong, G., Ko, J., ... & Knoll, F. (2021). Results of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction. IEEE Transactions on Medical Imaging, 40(9), pages 2306-2317.
  12. Johnson, P. M., Jeong, G., Hammernik, K., Schlemper, J., Qin, C., Duan, J., ..., & Knoll, F. (2021). Evaluation of the Robustness of Learned MR Image Reconstruction to Systematic Deviations Between Training and Test Data for the Models from the fastMRI Challenge. In MICCAI MLMIR Workshop, pages 25–34,
  13. Bakker, T., Muckley, M.J., Romero-Soriano, A., Drozdzal, M. & Pineda, L. (2022). On learning adaptive acquisition policies for undersampled multi-coil MRI reconstruction. In MIDL, pages 63-85.
  14. Radmanesh, A.*, Muckley, M. J.*, Murrell, T., Lindsey, E., Sriram, A., Knoll, F., ... & Lui, Y. W. (2022). Exploring the Acceleration Limits of Deep Learning VarNet-based Two-dimensional Brain MRI. Radiology: Artificial Intelligence, 4(6), page e210313.
  15. Johnson, P.M., Lin, D.J., Zbontar, J., Zitnick, C.L., Sriram, A., Muckley, M., Babb, J.S., Kline, M., Ciavarra, G., Alaia, E., ..., & Knoll, F. (2023). Deep Learning Reconstruction Enables Prospectively Accelerated Clinical Knee MRI. Radiology, 307(2), page e220425.
  16. Tibrewala, R., Dutt, T., Tong, A., Ginocchio, L., Keerthivasan, M.B., Baete, S.H., Lui, Y.W., Sodickson, D.K., Chandarana, H., Johnson, P.M. (2023). FastMRI Prostate: A Publicly Available, Biparametric MRI Dataset to Advance Machine Learning for Prostate Cancer Imaging. arXiv preprint, arXiv:2034.09254.

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