Motion represents one of the major challenges in magnetic resonance imaging (MRI). Since the MR signal is acquired in frequency space, any motion of the imaged object leads to complex artefacts in the reconstructed image in addition to other MR imaging artefacts. Deep learning has been frequently proposed for motion correction at several stages of the reconstruction process. The wide range of MR acquisition sequences, anatomies and pathologies of interest, and motion patterns (rigid vs. deformable and random vs. regular) makes a comprehensive solution unlikely. To facilitate the transfer of ideas between different applications, this review provides a detailed overview of proposed methods for learning-based motion correction in MRI together with their common challenges and potentials. This review identifies differences and synergies in underlying data usage, architectures, training and evaluation strategies. We critically discuss general trends and outline future directions, with the aim to enhance interaction between different application areas and research fields.

翻译：运动是磁共振成像（MRI）面临的主要挑战之一。由于MR信号在频率空间采集，被成像物体的任何运动除了会产生其他MR成像伪影外，还会在重建图像中导致复杂的伪影。深度学习已被频繁提出用于重建过程中多个阶段的运动校正。由于MR采集序列、解剖结构、病理特征以及运动模式（刚体与形变、随机与规律）的广泛多样性，难以构建一个全面的解决方案。为促进不同应用领域之间的思想交流，本综述详细概述了基于学习的MRI运动校正方法及其面临的共同挑战与潜在优势。本文识别了底层数据使用、网络架构、训练与评估策略方面的差异与协同效应。我们批判性地讨论了总体趋势并指出了未来方向，旨在增强不同应用领域与研究领域之间的交互。