Abstract: IM-MoCo - Self-supervised MRI Motion Correction using Motion-guided Implicit Neural Representations

Ziad Al-Haj Hemidi, Christian Weihsbach, Mattias P. Heinrich

Abstract: Motion artifacts in magnetic resonance imaging (MRI) arise from long acquisition times and can compromise the clinical utility of the images obtained. Traditional motion correction methods often struggle with severe motion, leading to distorted and unreliable results. Deep learning (DL) has improved upon these limitations through generalization, even though it also comes with challenges, such as vanishing structures and hallucinations.