  Motion artifact is a major challenge in magnetic resonance imaging (MRI) that
severely degrades image quality, reduces examination efficiency, and makes
accurate diagnosis difficult. However, previous methods often relied on
implicit models for artifact correction, resulting in biases in modeling the
artifact formation mechanism and characterizing the relationship between
artifact information and anatomical details. These limitations have hindered
the ability to obtain high-quality MR images. In this work, we incorporate the
artifact generation mechanism to reestablish the relationship between artifacts
and anatomical content in the image domain, highlighting the superiority of
explicit models over implicit models in medical problems. Based on this, we
propose a novel end-to-end image domain model called AF2R, which addresses this
problem using conditional normalization flow. Specifically, we first design a
feature encoder to extract anatomical features from images with motion
artifacts. Then, through a series of reversible transformations using the
feature-to-image flow module, we progressively obtain MR images unaffected by
motion artifacts. Experimental results on simulated and real datasets
demonstrate that our method achieves better performance in both quantitative
and qualitative results, preserving better anatomical details.
