{"doi":"10.1002/mrm.70026","title":"Enhancing cardiac <scp>MRI</scp> reliability at <scp>3 T</scp> using motion‐adaptive <scp> B <sub>0</sub> </scp> shimming","abstract":"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 &lt; 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 &lt; 0.05) and expiratory (0.65 ± 0.16 vs. 0.84 ± 0.20; p &lt; 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 &lt; 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.","journal":"Magnetic Resonance in Medicine","year":2025,"id":528666,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":2,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9438,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1312483,"name":"Archana Malagi","orcid":"0000-0002-2332-170X","position":1,"is_corresponding":false},{"id":1312482,"name":"Xinqi Li","orcid":"0009-0002-4799-8333","position":2,"is_corresponding":false},{"id":734153,"name":"Xingmin Guan","orcid":"0000-0002-3698-1811","position":3,"is_corresponding":false},{"id":1312484,"name":"Chia-Chi Yang","orcid":"0000-0003-0933-7067","position":4,"is_corresponding":false},{"id":1312485,"name":"Li‐Ting Huang","orcid":"0009-0007-1170-8767","position":5,"is_corresponding":false},{"id":1317662,"name":"Ziyang Long","orcid":null,"position":6,"is_corresponding":false},{"id":913655,"name":"Jeremy Zepeda","orcid":"0000-0001-8955-2992","position":7,"is_corresponding":false},{"id":1266691,"name":"Xinheng Zhang","orcid":"0000-0001-6409-3160","position":8,"is_corresponding":false},{"id":1267112,"name":"Ghazal Yoosefian","orcid":null,"position":9,"is_corresponding":false},{"id":383639,"name":"Xiaoming Bi","orcid":"0000-0001-6286-9172","position":10,"is_corresponding":false},{"id":1312486,"name":"Chang Gao","orcid":"0000-0001-7825-0024","position":11,"is_corresponding":false},{"id":1123644,"name":"Yun Shang","orcid":"0000-0003-3429-7730","position":12,"is_corresponding":false},{"id":1317664,"name":"Nader Binesh","orcid":null,"position":13,"is_corresponding":false},{"id":730382,"name":"Hsu‐Lei Lee","orcid":"0000-0001-5509-5199","position":14,"is_corresponding":false},{"id":289229,"name":"Debiao Li","orcid":"0000-0001-8560-8231","position":15,"is_corresponding":false},{"id":422490,"name":"Rohan Dharmakumar","orcid":"0000-0003-4120-4635","position":16,"is_corresponding":false},{"id":1156165,"name":"Hui Han","orcid":"0000-0003-4440-4421","position":17,"is_corresponding":false},{"id":512008,"name":"Hsin-Jung Yang","orcid":"0000-0002-5576-9568","position":18,"is_corresponding":false},{"id":1266690,"name":"Yuheng Huang","orcid":"0009-0000-2890-2442","position":0,"is_corresponding":true}],"reference_count":27,"raw_metadata":null,"created_at":"2026-07-19T02:50:48.492873Z","pmid":"40810283","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}