Abstract
PURPOSE: Magnetic susceptibility differences at the heart-lung interface introduce B(0)-field inhomogeneities that challenge cardiac MRI at high field strengths (≥ 3 T). Although hardware-based shimming has advanced, conventional approaches often neglect dynamic variations in thoracic anatomy caused by cardiac and respiratory motion, leading to residual off-resonance artifacts. This study aims to characterize motion-induced B(0)-field fluctuations in the heart and evaluate a deep learning-enabled motion-adaptive B(0) shimming pipeline to mitigate them. METHODS: A motion-resolved B(0) mapping sequence was implemented at 3 T to quantify cardiac and respiratory-induced B(0) variations. A motion-adaptive shimming framework was then developed and validated through numerical simulations and human imaging studies. B(0)-field homogeneity and T(2)* mapping accuracy were assessed in multiple breath-hold positions using standard and motion-adaptive shimming. RESULTS: Respiratory motion significantly altered myocardial B(0) fields (p < 0.01), whereas cardiac motion had minimal impact (p = 0.49). Compared with conventional scanner shimming, motion-adaptive B(0) shimming yielded significantly improved field uniformity across both inspiratory (post-shim SD(ratio): 0.68 ± 0.10 vs. 0.89 ± 0.11; p < 0.05) and expiratory (0.65 ± 0.16 vs. 0.84 ± 0.20; p < 0.05) breath-hold states. Corresponding improvements in myocardial T(2)* map homogeneity were observed, with reduced coefficient of variation (0.44 ± 0.19 vs. 0.39 ± 0.22; 0.59 ± 0.30 vs. 0.46 ± 0.21; both p < 0.01). CONCLUSION: The proposed motion-adaptive B(0) shimming approach effectively compensates for respiration-induced B(0) fluctuations, enhancing field homogeneity and reducing off-resonance artifacts. This strategy improves the robustness and reproducibility of T(2)* mapping, enabling more reliable high-field cardiac MRI.