December 4, 2025Open Access

Comprehensive Review of Non-Clinical MRI Motion Correction: Hardware, Retrospective, and AI-Based Approaches

Key Points

AI-based methods improve image quality by effectively correcting motion artifacts, enhancing the fidelity of MRI images.
High resolution imaging techniques and patient compliance are critical factors highlighted for successful MRI applications.
The review focuses on integrating hardware and stepwise retrospective approaches, while providing insights into engineering and computational aspects.
Improved strategies are crucial for research settings and for accommodating the needs of pediatric populations with involuntary motion.

Abstract

This review provides a detailed overview of motion correction strategies in magnetic resonance imaging (MRI) outside of standard clinical interventions. It covers hardware-based techniques, prospective and retrospective correction methods, and the emerging role of artificial intelligence in mitigating motion artifacts. By emphasizing engineering, computational, and data driven approaches, the review highlights how motion correction can enhance image quality, support high resolution and quantitative imaging, and reduce reliance on patient compliance. This work serves as a resource for researchers and practitioners aiming to improve MRI fidelity in research settings, pediatric populations, and subjects with involuntary motion.

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