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Computers, Volume 15, Issue 1

2026 January - 70 articles

Cover Story: AI increasingly reshapes control mechanisms governing MRI, enabling faster, safer, and more adaptive operation of the scanner’s subsystems. This review surveys AI-driven advances in control design, SAR prediction, motion-dependent field modeling, and gradient system characterization and correction. Neural networks act as efficient surrogates for lengthy simulations, motion tracking, and gradient modeling, delivering subject-specific predictions in subseconds, reducing the need for extensive calibration and promoting real-time, seamless integration of new applications. Remaining challenges include unified multiphysics models and generalization across systems and scan modalities. Yet, these advances position AI-driven control as a cornerstone of next-generation, personalized MRI. View this paper
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Computers - ISSN 2073-431X
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