fastMRI: An Open Dataset and Benchmarks for Accelerated MRI
@article{Zbontar2018fastMRIAO, title={fastMRI: An Open Dataset and Benchmarks for Accelerated MRI}, author={Jure Zbontar and Florian Knoll and Anuroop Sriram and Matthew 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 G. 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}, year={2018}, volume={abs/1811.08839} }
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…
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