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.
翻译:加速磁共振成像扫描是磁共振研究界的主要未决问题之一。为实现这一目标,我们主办了第二次快速磁共振成像竞赛,目的是利用次抽样K空间数据重建磁共振图像。我们向参与者提供了7 299个临床脑扫描(通过纽约州兰格内健康协会的符合HIPAA程序确定)的数据,这些扫描中的全抽成像数据保留了894个数据,以进行挑战评价。与2019年的挑战相反,我们把放射科评价的重点放在脑图象病理评估上。我们还开辟了一个新的传输轨迹,要求参与者在数据集外的磁共振成像仪扫描仪上提交模型。我们收到了8个不同组的19份呈文。结果显示,1个团队在SSIM分数和定性放射学家评价中得分都是最佳的。我们还分析了减轻背景噪音影响的替代指标,并收集了参与者的反馈,以告知未来的挑战。最后,我们查明了提交材料中常见的失败模式,突出了MRI重建界未来需要研究的领域。