Safari Mojtaba, Eidex Zach, Qiu Richard L J, Goette Matthew, Wang Tonghe, Yang Xiaofeng
Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, 30322, GA, USA.
Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, 10065, NY, USA.
ArXiv. 2025 Sep 5:arXiv:2509.05071v1.
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.
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.
DL, particularly generative models, shows 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.
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.
系统评价并进行荟萃分析人工智能（AI）驱动的检测和校正磁共振成像（MRI）运动伪影的方法，评估当前的进展、有效性、挑战和未来研究方向。
进行了全面的系统评价和荟萃分析，重点关注深度学习（DL）方法，特别是生成模型，用于检测和校正MRI运动伪影。提取了关于使用的数据集、DL架构和性能指标的定量数据。
DL，特别是生成模型，在减少运动伪影和提高图像质量方面显示出前景；然而，有限的通用性、对配对训练数据的依赖以及视觉失真风险仍然是推动标准化数据集和报告的关键挑战。
AI驱动的方法，特别是DL生成模型，通过有效解决运动伪影在改善MRI图像质量方面显示出巨大潜力。然而，必须解决关键挑战，包括需要全面的公共数据集、伪影水平的标准化报告协议，以及更先进、适应性更强的DL技术以减少对大量配对数据集的依赖。解决这些方面可以大幅提高MRI诊断准确性、降低医疗成本并改善患者护理结果。
