Motion artifacts are a frequent source of image degradation in clinical practice. Here we demonstrate the feasibility of correcting motion artifacts in magnitude-only MR images using a multi-resolution fully convolutional neural network. Training and testing datasets were generated using artificially created artifacts introduced onto in vivo clinical brain scans. Both the corrupted input and filtered output images were rated by an experienced neuroradiologist.
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