MAMOC：基于掩码自编码的MRI运动校正
MAMOC: MRI Motion Correction via Masked Autoencoding
AI总结：
针对MRI扫描运动伪影问题，提出基于掩码自编码自监督、迁移学习和测试时预测的MAMOC方法，首次用公开真实运动数据集评估，性能优于现有运动校正方法。
摘要：
磁共振成像（MRI）扫描中存在的运动伪影构成了重大挑战，哪怕患者轻微移动都可能产生伪影，损害扫描的实用价值。本文提出了MAsked MOtion Correction（MAMOC，掩码运动校正）这一新型方法，用于解决受运动影响的脑部MRI扫描中的回顾性伪影校正（Retrospective Artifact Correction, RAC）问题。MAMOC采用掩码自编码自监督、迁移学习和测试时预测技术，可高效去除运动伪影，生成高保真的原生分辨率扫描图像。此前，用于训练和评估回顾性运动校正方法的逼真公开配对伪影数据集尚不存在，因此必须模拟运动伪影。本研究利用MR-ART数据集以及更大规模的无标注数据集（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的性能优于现有运动校正方法。
英文摘要：
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.
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