Realistic Restorer: artifact-free flow restorer(AF2R) for MRI motion artifact removal

• URL: http://arxiv.org/abs/2306.10689v1
• Date: Mon, 19 Jun 2023 04:02:01 GMT
• Title: Realistic Restorer: artifact-free flow restorer(AF2R) for MRI motion artifact removal
• Authors: Jiandong Su and Kun Shang and Dong Liang
• Abstract summary: Motion artifact severely degrades image quality, reduces examination efficiency, and makes accurate diagnosis difficult. Previous methods often relied on implicit models for artifact correction, resulting in biases in modeling the artifact formation mechanism. We incorporate the artifact generation mechanism to reestablish the relationship between artifacts and anatomical content in the image domain.
• Score: 3.8103327351507255
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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