  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.
