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, transfer learning and test-time prediction to efficiently remove motion artifacts, producing high-fidelity, native-resolution scans. Until recently, realistic, openly available paired artifact presentations for training and evaluating retrospective motion correction methods did not exist, making it necessary to simulate motion artifacts. Leveraging the MR-ART dataset and bigger unlabeled datasets (ADNI, OASIS-3, IXI), this work is the first to evaluate motion correction in MRI scans using real motion data on a public dataset, showing that MAMOC achieves improved performance over existing motion correction 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。本文提出了掩码运动校正（MAMOC）这一新方法，旨在解决受运动影响的脑部MRI扫描中的回顾性伪影校正（RAC）问题。MAMOC利用掩码自编码自监督学习、迁移学习和测试时预测来高效去除运动伪影，生成高保真、原生分辨率的扫描图像。直到最近，用于训练和评估回顾性运动校正方法的真实、公开可用的成对伪影数据仍不存在，这使得模拟运动伪影成为必要。本研究利用MR-ART数据集及更大规模的无标签数据集（ADNI、OASIS-3、IXI），首次在公开数据集上使用真实运动数据评估MRI扫描的运动校正效果，结果表明MAMOC在性能上优于现有运动校正方法。