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.

翻译：磁共振成像（MRI）扫描中运动伪影的存在构成了重大挑战，即使患者轻微移动也可能导致伪影，从而影响扫描的实用性。本文提出了一种新颖的掩码运动校正方法（MAMOC），旨在解决受运动影响的脑部MRI扫描中的回顾性伪影校正问题。MAMOC利用掩码自编码自监督与测试时预测，有效消除运动伪影，生成最先进的原始分辨率扫描。此前，评估回顾性运动校正方法缺乏真实数据，运动伪影必须通过模拟生成。本研究借助MR-ART数据集，首次使用真实运动数据评估MRI扫描中的运动校正，证明了MAMOC相对于现有运动校正方法的优越性。