Correcting motion-related B0 inhomogeneities in magnetic resonance imaging via combined spherical harmonics and AC/DC matrix coils using DL-based prediction–simulation study

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 0 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 0 changes. We simulated real-time shimming under motion using jointly first-order SPH and a 31-channel AC/DC matrix coil. Measured B 0 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 0 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.