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fastMRI: An Open Dataset and Benchmarks for Accelerated MRI

21 November 2018
Jure Zbontar
Florian Knoll
Anuroop Sriram
Tullie Murrell
Zhengnan Huang
Matthew Muckley
Aaron Defazio
Ruben Stern
Patricia M. Johnson
M. Bruno
Marc Parente
Krzysztof J. Geras
Joe Katsnelson
H. Chandarana
Zizhao Zhang
M. Drozdzal
Adriana Romero
Michael G. Rabbat
Pascal Vincent
N. Yakubova
James Pinkerton
Duo Wang
Erich Owens
C. L. Zitnick
M. Recht
D. Sodickson
Yvonne W. Lui
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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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