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 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：运动是磁共振成像中的主要挑战之一。由于MR信号在频率空间中进行采集，被成像对象的任何运动都会在重建图像中引入复杂伪影，此外还可能叠加其他MR成像伪影。深度学习已被频繁应用于重建过程的多个阶段以校正运动。由于MR采集序列、解剖结构及病理类型、运动模式（刚体与非刚体、随机与规律运动）的多样性，寻求一种通用解决方案并不现实。为促进不同应用场景之间的思想交流，本综述详细概述了基于学习的MRI运动校正方法，探讨其共性挑战与潜力。本文识别了底层数据使用、网络架构及评估策略中的差异与协同效应，并批判性地讨论了总体趋势与未来方向，旨在加强不同应用领域与研究领域之间的交互。