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

Retrospective Motion Correction in Gradient Echo MRI by Explicit Motion Estimation Using Deep CNNs