MAMOC: MRI Motion Correction via Masked Autoencoding

TL;DR

MAMOC is a novel MRI motion correction method that employs masked autoencoding, transfer learning, and test-time prediction to effectively remove motion artifacts, improving scan quality using real motion data.

Contribution

This work introduces MAMOC, the first to evaluate MRI motion correction with real motion data on public datasets, advancing retrospective artifact correction techniques.

Findings

MAMOC outperforms existing correction methods.

Uses real motion data for evaluation.

Leverages large unlabeled datasets for training.

Abstract

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.