  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.
