论文标题
2020年FastMRI挑战的结果MR图像重建
Results of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction
论文作者
论文摘要
加速MRI扫描是MRI研究界的主要问题之一。为了实现这一目标，我们举办了针对使用亚k空间数据重建MR图像的第二个FASTMRI竞赛。我们为参与者提供了7,299次临床脑部扫描的数据（通过NYU Langone Health通过符合HIPAA的程序来识别识别），阻止了这些扫描中894个扫描中完全采样的数据，以进行挑战评估目的。与2019年的挑战相反，我们将放射科医生评估集中在大脑图像中的病理评估上。我们还首次推出了一条新的转移轨道，要求参与者提交对培训集外的MRI扫描仪进行评估的模型。我们从八个不同的小组那里收到了19项意见书。结果表明，在SSIM分数和定性放射科医生评估中，一个团队得分最高。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，以告知未来的挑战。最后，我们确定了整个意见的常见失败模式，强调了MRI重建社区未来研究的需求领域。
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.
