Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challenge. Issue 6 (7th June 2020)

• Record Type: Journal Article
• Authors: Knoll, Florian; Murrell, Tullie; Sriram, Anuroop; Yakubova, Nafissa; Zbontar, Jure; Rabbat, Michael; Defazio, Aaron; Muckley, Matthew J.; Sodickson, Daniel K.; Zitnick, C. Lawrence; Recht, Michael P.
• Is Part Of: Magnetic resonance in medicine. Volume 84:Issue 6(2020)
• Page Start: 3054
• Page End: 3070
• Publication Date: 2020-06-07
• Subjects: challenge -- compressed sensing -- fast imaging -- image reconstruction -- machine learning, optimization -- parallel imaging -- public dataset; Nuclear magnetic resonance -- Periodicals; Electron paramagnetic resonance -- Periodicals; 616.07548
• DOI: 10.1002/mrm.28338
• Languages: English
• ISSNs: 0740-3194

Abstract

Abstract : Purpose: To advance research in the field of machine learning for MR image reconstruction with an open challenge. Methods: We provided participants with a dataset of raw k‐space data from 1, 594 consecutive clinical exams of the knee. The goal of the challenge was to reconstruct images from these data. In order to strike a balance between realistic data and a shallow learning curve for those not already familiar with MR image reconstruction, we ran multiple tracks for multi‐coil and single‐coil data. We performed a two‐stage evaluation based on quantitative image metrics followed by evaluation by a panel of radiologists. The challenge ran from June to December of 2019. Results: We received a total of 33 challenge submissions. All participants chose to submit results from supervised machine learning approaches. Conclusions: The challenge led to new developments in machine learning for image reconstruction, provided insight into the current state of the art in the field, and highlighted remaining hurdles for clinical adoption.