Results of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction

Matthew J. Muckley, Bruno Riemenschneider, Alireza Radmanesh, Sunwoo Kim, Geunu Jeong, Jingyu Ko, Yohan Jun, Hyungseob Shin, Dosik Hwang, Mahmoud Mostapha, Simon Arberet, Dominik Nickel, Zaccharie Ramzi, Philippe Ciuciu, Jean Luc Starck, Jonas Teuwen, Dimitrios Karkalousos, Chaoping Zhang, Anuroop Sriram, Zhengnan HuangNafissa Yakubova, Yvonne W. Lui, Florian Knoll

Research output: Contribution to journalReview articlepeer-review

20 Citations (Scopus)


Accelerating MRI scans is one of the principal outstanding problems in the MRI research community. Towards this goal, we hosted the second fastMRI competition targeted towards reconstructing MR images with subsampled k-space data. We provided participants with data from 7,299 clinical brain scans (de-identified via a HIPAA-compliant procedure by NYU Langone Health), holding back the fully-sampled data from 894 of these scans for challenge evaluation purposes. In contrast to the 2019 challenge, we focused our radiologist evaluations on pathological assessment in brain images. We also debuted a new Transfer track that required participants to submit models evaluated on MRI scanners from outside the training set. We received 19 submissions from eight different groups. Results showed one team scoring best in both SSIM scores and qualitative radiologist evaluations. We also performed analysis on alternative metrics to mitigate the effects of background noise and collected feedback from the participants to inform future challenges. Lastly, we identify common failure modes across the submissions, highlighting areas of need for future research in the MRI reconstruction community.

Original languageEnglish
Article number9420272
Pages (from-to)2306-2317
Number of pages12
JournalIEEE Transactions on Medical Imaging
Issue number9
Publication statusPublished - 2021 Sep

Bibliographical note

Funding Information:
Manuscript received March 5, 2021; revised April 21, 2021; accepted April 22, 2021. Date of publication April 30, 2021; date of current version August 31, 2021. This work was supported by the NIH under Award R01EB024532, Award P41EB017183, and Award R21EB027241. The NIH funding was awarded to NYU. (Matthew J. Muckley and Bruno Riemenschneider contributed equally to this work.) (Corresponding author: Matthew J. Muckley.) This work involved human subjects or animals in its research. Approval of all ethical and experimental procedures and protocols was granted by the NYU Grossman School of Medicine Internal Review Board.

Publisher Copyright:
© 1982-2012 IEEE.

All Science Journal Classification (ASJC) codes

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering


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