Correcting motion-related B<sub>0</sub> inhomogeneities in magnetic resonance imaging via combined spherical harmonics and AC/DC matrix coils using DL-based prediction-simulation study.
Authors
Affiliations (10)
Affiliations (10)
- Department of Medical Engineering, Carinthia University of Applied Sciences, Klagenfurt, Austria.
- Department of Biomedical Imaging and Image-Guided Therapy, High Field MR Center, Medical University of Vienna, Vienna, Austria.
- Department of Biomedical Imaging and Image-Guided Therapy, High Field MR Center, Medical University of Vienna, Vienna, Austria. [email protected].
- Christian Doppler Laboratory for MR Imaging Biomarkers (BIOMAK), Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria. [email protected].
- Comprehensive Center of Artificial Intelligence in Medicine (CAIM), Medical University of Vienna, Vienna, Austria. [email protected].
- Harvard Medical School, Boston, MA, USA.
- Department of Radiology, A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.
- Comprehensive Center of Artificial Intelligence in Medicine (CAIM), Medical University of Vienna, Vienna, Austria.
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Lazarettgasse 14, Vienna, Austria.
- Christian Doppler Laboratory for MR Imaging Biomarkers (BIOMAK), Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria.
Abstract
MR data quality depends on static magnetic field (B<sub>0</sub>) homogeneity. At the beginning of each session, a brief field map quantifies subject-specific B<sub>0</sub> 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<sub>0</sub> changes. We simulated real-time shimming under motion using jointly first-order SPH and a 31-channel AC/DC matrix coil. Measured B<sub>0</sub> 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<sub>0</sub> 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<sub>0</sub> changes provides a practical path to sequence-agnostic, motion-robust shimming across a broad range of MR protocols.