School of Biomedical Engineering, ShanghaiTech University, 4th Floor, BME Building, 393 Middle Huaxia Road, Pudong District, Shanghai, 201210, China.
Shanghai Clinical Research and Trial Center, ShanghaiTech University, Shanghai, China.
MAGMA. 2024 Jul;37(3):397-409. doi: 10.1007/s10334-023-01144-5. Epub 2024 Feb 22.
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.
目标运动是磁共振成像（MRI）中的一个长期存在的问题，会严重降低图像质量。已经提出了各种前瞻性和回顾性方法来进行 MRI 运动校正，其中深度学习方法取得了最先进的运动校正性能。本综述论文旨在提供基于深度学习的 MRI 运动校正方法的全面回顾。详细介绍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，除了运动校正的 MRI 重建外，还简要介绍了如何将估计的运动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。
