Artificial intelligence (AI) is increasingly reshaping the control mechanisms that govern magnetic resonance imaging (MRI), enabling faster, safer, and more adaptive operation of the scanner’s physical subsystems. This review provides a comprehensive survey of recent AI-driven advances in core control domains: radio frequency (RF) pulse design and specific absorption rate (SAR) prediction, motion-dependent modeling of B1+ and B0 fields, and gradient system characterization and correction. Across these domains, deep learning models—convolutional, recurrent, generative, and temporal convolutional networks—have emerged as powerful computational surrogates for numerical electromagnetic simulations, Bloch simulations, motion tracking, and gradient impulse response modeling. These networks achieve subject-specific field or SAR predictions within seconds or milliseconds, mitigating long-standing limitations associated with inter-subject variability, non-linear system behavior, and the need for extensive calibration. We highlight methodological themes such as physics-guided training, reinforcement learning for RF pulse design, subject-specific fine-tuning, uncertainty considerations, and the integration of learned models into real-time MRI workflows. Open challenges and future directions include unified multi-physics frameworks, deep learning approaches for generalizing across anatomies and coil configurations, robust validation across vendors and field strengths, and safety-aware AI design. Overall, AI-powered control strategies are poised to become foundational components of next-generation, high-performance, and personalized MRI systems.
Vinding et al. (Mon,) studied this question.