{"doi":"10.1002/mrm.29351","title":"Free‐breathing, non‐<scp>ECG</scp>, simultaneous myocardial <scp>T<sub>1</sub></scp>, <scp>T<sub>2</sub></scp>, <scp>T<sub>2</sub></scp>*, and fat‐fraction mapping with motion‐resolved cardiovascular MR multitasking","abstract":"Purpose To develop a free‐breathing, non‐electrocardiogram technique for simultaneous myocardial T 1 , T 2 , T 2 *, and fat‐fraction (FF) mapping in a single scan. Methods The MR Multitasking framework is adapted to quantify T 1 , T 2 , T 2 *, and FF simultaneously. A variable TR scheme is developed to preserve temporal resolution and imaging efficiency. The underlying high‐dimensional image is modeled as a low‐rank tensor, which allows accelerated acquisition and efficient reconstruction. The accuracy and/or repeatability of the technique were evaluated on static and motion phantoms, 12 healthy volunteers, and 3 patients by comparing to the reference techniques. Results In static and motion phantoms, T 1 /T 2 /T 2 */FF measurements showed substantial consistency ( R &gt; 0.98) and excellent agreement (intraclass correlation coefficient &gt; 0.93) with reference measurements. In human subjects, the proposed technique yielded repeatable T 1 , T 2 , T 2 *, and FF measurements that agreed with those from references. Conclusions The proposed free‐breathing, non‐electrocardiogram, motion‐resolved Multitasking technique allows simultaneous quantification of myocardial T 1 , T 2 , T 2 *, and FF in a single 2.5‐min scan.","journal":"Magnetic Resonance in Medicine","year":2022,"id":253533,"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":22,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9481,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":383637,"name":"Nan Wang","orcid":"0000-0002-7616-8083","position":1,"is_corresponding":false},{"id":351244,"name":"Alan C. Kwan","orcid":"0000-0002-4393-1011","position":2,"is_corresponding":false},{"id":730382,"name":"Hsu‐Lei Lee","orcid":"0000-0001-5509-5199","position":3,"is_corresponding":false},{"id":770528,"name":"Xianglun Mao","orcid":"0000-0002-2745-7444","position":4,"is_corresponding":false},{"id":463629,"name":"Yibin Xie","orcid":"0000-0002-0333-567X","position":5,"is_corresponding":false},{"id":396971,"name":"Kim‐Lien Nguyen","orcid":"0000-0002-8854-2976","position":6,"is_corresponding":false},{"id":899481,"name":"Caroline M. Colbert","orcid":"0000-0003-0031-5339","position":7,"is_corresponding":false},{"id":383640,"name":"Fei Han","orcid":"0000-0002-1598-9048","position":8,"is_corresponding":false},{"id":730380,"name":"Pei Han","orcid":"0000-0001-8532-9339","position":9,"is_corresponding":false},{"id":604766,"name":"Hui Han","orcid":"0000-0002-8890-4295","position":10,"is_corresponding":false},{"id":383636,"name":"Anthony Christodoulou","orcid":"0000-0002-9334-8684","position":11,"is_corresponding":false},{"id":289229,"name":"Debiao Li","orcid":"0000-0001-8560-8231","position":12,"is_corresponding":false},{"id":693437,"name":"Tianle Cao","orcid":"0000-0003-3739-3103","position":0,"is_corresponding":true}],"reference_count":71,"raw_metadata":null,"created_at":"2026-07-19T00:24:59.596040Z","pmid":"35713184","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":[]}