Abstract
Abstract
MR data quality depends on static magnetic field (B
0
) homogeneity. At the beginning of each session, a brief field map quantifies subject-specific B
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variation, and shim coils are then set to counteract it. Conventional spherical-harmonic (SPH) shims have limited shimming power, motivating localized multi-coil (AC/DC) systems. However, subject motion can perturb the optimized field, necessitating real-time shim updates that require rapid tracking of B
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changes. We simulated real-time shimming under motion using jointly first-order SPH and a 31-channel AC/DC matrix coil. Measured B
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data initially shimmed with SPH were augmented with AC/DC terms in simulation, and real-time control was evaluated. Shimming with AC/DC coils added to the SPH coils improved field homogeneity, but motion eroded these gains. With simulated real-time updates informed by deep learning, B
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homogeneity was effectively maintained even during substantial motion. Performance matched simulated navigator-like real-time shimming with gradient-echo and echo-planar imaging, while adding no extra scan time in main imaging sequences. Multi-coil shimming offers clear benefits, but the gains may be reduced if shim terms are not updated in real time. Deep-learning-driven prediction of B
0
changes provides a practical path to sequence-agnostic, motion-robust shimming across a broad range of MR protocols.