Magnetic Resonance Imaging allows high resolution data acquisition with the downside of motion sensitivity due to relatively long acquisition times. Even during the acquisition of a single 2D slice, motion can severely corrupt the image. Retrospective motion correction strategies do not interfere during acquisition time but operate on the motion affected data. Known methods suited to this scenario are compressed sensing (CS), generative adversarial networks (GANs), and motion estimation. In this paper we propose a strategy to correct for motion artifacts using Deep Convolutional Neuronal Networks (Deep CNNs) in a reliable and verifiable manner by explicit motion estimation. The sensitivity encoding (SENSE) redundancy that multiple receiver coils provide, has in the past been used for acceleration, noise reduction and rigid motion compensation. We show that using Deep CNNs the concepts of rigid motion compensation can be generalized to more complex motion fields. Using a simulated synthetic data set, our proposed supervised network is evaluated on motion corrupted MRIs of abdomen and head. We compare our results with rigid motion compensation and GANs.

翻译：磁共振成像能够实现高分辨率数据采集，但因其较长的采集时间而对运动敏感。即使在单个二维切片采集过程中，运动也可能严重破坏图像质量。回顾性运动校正策略不干预采集过程，而是直接处理受运动影响的数据。适用于该场景的已知方法包括压缩感知(CS)、生成对抗网络(GANs)和运动估计。本文提出一种基于深度卷积神经网络(Deep CNNs)的可靠且可验证的运动伪影校正策略，通过显式运动估计实现。多接收线圈提供的灵敏度编码(SENSE)冗余性过去已被用于加速、降噪和刚性运动补偿。我们证明，利用深度CNN可将刚性运动补偿的概念推广至更复杂的运动场。基于模拟合成数据集，我们提出的监督网络在腹部和头部的运动伪影MRI上进行了评估，并与刚性运动补偿和GANs方法进行了比较。