Systematic Review and Meta-analysis of AI-driven MRI Motion Artifact Detection and Correction

• URL: http://arxiv.org/abs/2509.05071v1
• Date: Fri, 05 Sep 2025 13:09:37 GMT
• Title: Systematic Review and Meta-analysis of AI-driven MRI Motion Artifact Detection and Correction
• Authors: Mojtaba Safari, Zach Eidex, Richard L. J. Qiu, Matthew Goette, Tonghe Wang, Xiaofeng Yang,
• Abstract summary: Deep learning approaches, particularly generative models, show promise for reducing motion artifacts and improving image quality.<n>However, limited generalizability, reliance on paired training data, and risk of visual distortions remain key challenges that motivate standardized datasets and reporting.
• Score: 2.904150783490918
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Background: To systematically review and perform a meta-analysis of artificial intelligence (AI)-driven methods for detecting and correcting magnetic resonance imaging (MRI) motion artifacts, assessing current developments, effectiveness, challenges, and future research directions. Methods: A comprehensive systematic review and meta-analysis were conducted, focusing on deep learning (DL) approaches, particularly generative models, for the detection and correction of MRI motion artifacts. Quantitative data were extracted regarding utilized datasets, DL architectures, and performance metrics. Results: DL, particularly generative models, show promise for reducing motion artifacts and improving image quality; however, limited generalizability, reliance on paired training data, and risk of visual distortions remain key challenges that motivate standardized datasets and reporting. Conclusions: AI-driven methods, particularly DL generative models, show significant potential for improving MRI image quality by effectively addressing motion artifacts. However, critical challenges must be addressed, including the need for comprehensive public datasets, standardized reporting protocols for artifact levels, and more advanced, adaptable DL techniques to reduce reliance on extensive paired datasets. Addressing these aspects could substantially enhance MRI diagnostic accuracy, reduce healthcare costs, and improve patient care outcomes.

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