Reliable Off-Resonance Correction in High-Field Cardiac MRI Using Autonomous Cardiac B 0 Segmentation with Dual-Modality Deep Neural Networks.

B 0 field inhomogeneity is a long-lasting issue for Cardiac MRI (CMR) in high-field (3T and above) scanners. The inhomogeneous B 0 fields can lead to corrupted image quality, prolonged scan time, and false diagnosis. B 0 shimming is the most straightforward way to improve the B 0 homogeneity. However, today's standard cardiac shimming protocol requires manual selection of a shim volume, which often falsely includes regions with large B 0 deviation (e.g., liver, fat, and chest wall). The flawed shim field compromises the reliability of high-field CMR protocols, which significantly reduces the scan efficiency and hinders its wider clinical adoption. This study aims to develop a dual-channel deep learning model that can reliably contour the cardiac region for B 0 shim without human interaction and under variable imaging protocols. By utilizing both the magnitude and phase information, the model achieved a high segmentation accuracy in the B 0 field maps compared to the conventional single-channel methods (Dice score: 2D-mag = 0.866, 3D-mag = 0.907, and 3D-mag-phase = 0.938, all p < 0.05). Furthermore, it shows better generalizability against the common variations in MRI imaging parameters and enables significantly improved B 0 shim compared to the standard method (SD( B 0 Shim): Proposed = 15 ± 11% vs. Standard = 6 ± 12%, p < 0.05). The proposed autonomous model can boost the reliability of cardiac shimming at 3T and serve as the foundation for more reliable and efficient high-field CMR imaging in clinical routines. [ABSTRACT FROM AUTHOR]