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.
Ramesh, Divya (Sun,) studied this question.