Abstract：
Subject motion is a long-standing problem of magnetic resonance imaging (MRI), which can seriously deteriorate the image quality. Various prospective and retrospective methods have been proposed for MRI motion correction, among which deep learning approaches have achieved state-of-the-art motion correction performance. This survey paper aims to provide a comprehensive review of deep learning-based MRI motion correction methods. Neural networks used for motion artifacts reduction and motion estimation in the image domain or frequency domain are detailed. Furthermore, besides motion-corrected MRI reconstruction, how estimated motion is applied in other downstream tasks is briefly introduced, aiming to strengthen the interaction between different research areas. Finally, we identify current limitations and point out future directions of deep learning-based MRI motion correction.
摘要：
受试者运动是磁共振成像（MRI）的一个长期存在的问题，这会严重恶化图像质量。已经提出了各种前瞻性和回顾性方法用于MRI运动校正，其中深度学习方法已经实现了最先进的运动校正性能。这篇调查论文旨在全面回顾基于深度学习的MRI运动矫正方法。详细描述了用于图像域或频域中的运动伪影减少和运动估计的神经网络。此外，除了运动校正MRI重建,简要介绍了估计运动如何应用于其他下游任务，旨在加强不同研究领域之间的互动。最后,我们确定了当前的局限性，并指出了基于深度学习的MRI运动校正的未来方向。
