The presence of motion artifacts in magnetic resonance imaging (MRI) scans
poses a significant challenge, where even minor patient movements can lead to
artifacts that may compromise the scan's utility. This paper introduces Masked
Motion Correction (MAMOC), a novel method designed to address the issue of
Retrospective Artifact Correction (RAC) in motion-affected MRI brain scans.
MAMOC uses masked autoencoding self-supervision and test-time prediction to
efficiently remove motion artifacts, producing state-of-the-art, native
resolution scans. Until recently, realistic data to evaluate retrospective
motion correction methods did not exist, motion artifacts had to be simulated.
Leveraging the MR-ART dataset, this work is the first to evaluate motion
correction in MRI scans using real motion data, showing the superiority of
MAMOC to existing motion correction (MC) methods.

该论文介绍了一种名为 MAMOC 的新方法，它使用具有掩码自编码自监督和测试时间预测的方法来解决动作对 MRI 脑部扫描的影响，并利用真实运动数据评估了 MAMOC 在 MRI 扫描中的运动校正能力，展示了其优越性。