IEEE Trans Med Imaging. 2021 Sep;40(9):2306-2317. doi: 10.1109/TMI.2021.3075856. Epub 2021 Aug 31.
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.
加速 MRI 扫描是 MRI 研究社区的主要未解决问题之一。为此，我们举办了第二届 fastMRI 竞赛，旨在使用欠采样 k 空间数据重建磁共振图像。我们向参与者提供了来自 7299 例临床脑部扫描的数据（通过纽约大学朗格尼健康中心的 HIPAA 合规程序进行去识别），并保留了其中 894 例扫描的全采样数据用于挑战赛评估。与 2019 年的挑战赛相比，我们将放射科医生的评估重点放在了脑部图像的病理评估上。我们还首次推出了一个新的转移轨道，要求参与者提交在训练集之外的 MRI 扫描仪上评估的模型。我们收到了来自八个不同团队的 19 份参赛作品。结果显示，一个团队在 SSIM 得分和定性放射科医生评估方面得分最高。我们还对替代指标进行了分析，以减轻背景噪声的影响，并从参与者那里收集反馈信息，为未来的挑战赛提供参考。最后，我们确定了提交作品中的常见故障模式，突出了 MRI 重建社区未来研究的需求领域。
