Severe MR Motion Artefact Correction with 2 step Deep Learning-based guidance

Motion artifacts can pose a difficult challenge in the clinical workflow. For addressing this issue, we here investigate the performance of two Deep Learning based motion mitigation strategies, MoPED and NAMER, and demonstrate that both approaches can readily be combined. This allows for the correction of severely corrupted images.