Patient motion during MR data acquisition appears in the reconstructed image as blurring and incoherent artefacts. In this work, we present a novel deep learning encoder-decoder convolutional neural network (CNN) that recasts the problem of motion correction into an artefact reduction problem. A CNN was designed and trained on simulated motion corrupted images that learned to remove the motion artefact. The motion compensated image reconstruction was transformed into quantized pixel classification, where each pixel in the motion corrupted MR image was classified to its uncorrupted quantized value using a trained deep learning encoder-decoder CNN.
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