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• Review •
Application and prospects of deep learning-based MRI motion artifact correction technology
ZHENG Danqun  LI Shuai  YU Ziqin  LI Xuezhou  BIAN Yun 

DOI:10.12015/issn.1674-8034.2026.02.033.


[Abstract] Magnetic resonance imaging (MRI) is susceptible to motion artifacts, which degrade image quality and diagnostic accuracy. Although traditional motion artifact correction techniques can improve image quality to a certain extent, they often have limited effectiveness and incur high processing costs, and these techniques are also unable to efficiently handle complex motion patterns and artifact types. Deep learning techniques, by virtue of their powerful feature learning capabilities, have provided new solutions to this problem. In recent years, the number of studies focusing on deep learning–based motion artifact correction has steadily increased; however, substantial heterogeneity remains among existing studies in terms of technical paradigms, research pathways, and application scenarios, and a systematic synthesis is still lacking. Although several reviews have summarized and discussed research in this field both domestically and internationally, current works remain limited by insufficient analysis of shared characteristics and innovative aspects across different approaches and network architectures, as well as by relatively narrow organizational logic or constrained research settings. Therefore, this article presents a systematic review of deep learning-based MRI motion artifact correction techniques developed over the past decade, and analyzes the limitations of current methods with respect to data acquisition, generalization capability, and clinical translation. This review aims to provide a structured technical reference and new research perspectives for future studies on deep learning-based MRI motion artifact correction, while also offering insights for the application of these techniques in clinical image quality optimization and diagnostic practice.
[Keywords] magnetic resonance imaging；motion artifacts；deep learning；artificial intelligence；image quality assessment

ZHENG Danqun   LI Shuai   YU Ziqin   LI Xuezhou   BIAN Yun*  

Department of Radiology, First Affiliated Hospital of Naval Medical University, Shanghai 200433, China

Corresponding author: BIAN Y, E-mail: bianyun2012@foxmail.com

Conflicts of interest   None.

Received  2025-10-14
Accepted  2026-01-31
DOI: 10.12015/issn.1674-8034.2026.02.033
DOI:10.12015/issn.1674-8034.2026.02.033.

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