fastMRI: An Open Dataset and Benchmarks for Accelerated MRI

Jure Zbontar, Florian Knoll, Anuroop Sriram, Tullie Murrell, Zhengnan Huang +22 more
11/21/2018
cs.CVcs.LGeess.SP

Official code: facebookresearch/fastMRI (1,537 stars, MIT, PyTorch, last commit 2025-01-21).

Abstract

Accelerating Magnetic Resonance Imaging (MRI) by taking fewer measurements has the potential to reduce medical costs, minimize stress to patients and make MRI possible in applications where it is currently prohibitively slow or expensive. We introduce the fastMRI dataset, a large-scale collection of both raw MR measurements and clinical MR images, that can be used for training and evaluation of machine-learning approaches to MR image reconstruction. By introducing standardized evaluation criteria and a freely-accessible dataset, our goal is to help the community make rapid advances in the state of the art for MR image reconstruction. We also provide a self-contained introduction to MRI for machine learning researchers with no medical imaging background.

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Code Implementations(1)

facebookresearch/fastMRIOfficial100%
PyTorchMIT

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

1,537422PythonOct 26, 20181 years agoMIT
convolutional-neural-networksdeep-learningfastmrifastmri-challengefastmri-dataset+4 more

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Cite this paper

@article{zbontar2018fastmri,
  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},
  year   = {2018},
  eprint = {1811.08839},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/1811.08839}
}

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