fastMRI: An Open Dataset and Benchmarks for Accelerated MRI

Zbontar, Jure, Knoll, Florian, Sriram, Anuroop, Muckley, Matthew J., Bruno, Mary, Defazio, Aaron, Parente, Marc, Geras, Krzysztof J., Katsnelson, Joe, Chandarana, Hersh, Zhang, Zizhao, Drozdzal, Michal, Romero, Adriana, Rabbat, Michael, Vincent, Pascal, Pinkerton, James, Wang, Duo, Yakubova, Nafissa, Owens, Erich, Zitnick, C. Lawrence, Recht, Michael P., Sodickson, Daniel K., Lui, Yvonne W.

arXiv.org Machine Learning 

The excellent soft tissue contrast and flexibility of magnetic resonance imaging (MRI) makes it a very powerful diagnostic tool for a wide range of disorders, including neurological, musculoskeletal, and oncological diseases. However, the long acquisition time in MRI, which can easily exceed 30 minutes, leads to low patient throughput, problems with patient comfort and compliance, artifacts from patient motion, and high exam costs. As a consequence, increasing imaging speed has been a major ongoing research goal since the advent of MRI in the 1970s. Increases in imaging speed have been achieved through both hardware developments (such as improved magnetic field gradients) and software advances (such as new pulse sequences). One noteworthy development in this context is parallel imaging, introduced in the 1990s, which allows multiple data points to be sampled simultaneously, rather than in a traditional sequential order [39, 26, 9].

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