Deep learning (DL) models for accelerated image reconstruction involves retrospective undersampling of the fully sampled k-space data for training and validation. This strategy is not a true reflection of real-world data and in many instances, the input k-space data is corrupted with artifacts and errors, such as motion artifacts. In this work, we propose to improve existing methods of DL training and validation by incorporating a motion layer during the training process. The incorporation of a motion layer makes the DL model aware of the underlying motion and results in improved image reconstruction in the presence of motion.
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