Deep learning solutions have been proposed to correct motion-induced artefact in free-breathing abdominal MRI that can overcome some challenges in conventional methods, such as requiring respiratory modeling, external motion monitoring devices, or extremely long computation time. However, ground truth data for training can only be acquired with breath-holding or using those conventional methods. In this study, we proposed to use FD-UNet for the motion correction of free-breathing abdominal MRI. The high-quality and artifact-free ground truth data were produced from repeated k-t-subsampling and greedy randomized adaptive search procedure (ReK-GRASP), without relying on respiratory modeling, external devices, or long computation time.
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