{"doi":"10.1002/mrm.30582","title":"Initial experience of cardiac <scp>T</scp> <sub>1</sub> ρ mapping at 0.55 <scp>T</scp> : Continuous wave versus adiabatic spin‐lock preparation pulses","abstract":"Abstract Purpose To propose and validate a cardiac T 1 ρ mapping sequence at 0.55 T comparing continuous‐wave and adiabatic spin‐lock (SL) preparation pulses. Methods The proposed 2D sequence acquires four single‐shot balanced SSFP readout images with differing contrasts in a single breath‐hold. The first three images are prepared with T 1 ρ preparation pulses with different durations, while the last image uses a saturation pulse immediately before data acquisition. The T 1 ρ map is calculated using a 3‐parameter fitting method. Bloch equation simulations were performed to optimize the parameters of the adiabatic‐SL pulses. Phantom studies and in vivo experiments in 10 healthy volunteers, a porcine myocardial infarction model, and a patient with suspected hypertrophic cardiomyopathy were performed to validate the performance of the proposed adiabatic T 1 ρ (T 1 ρ Ad ) mapping in comparison with conventional continuous‐wave T 1 ρ (T 1 ρ CW ) mapping. Results The adiabatic‐SL pulse with simulation‐optimized parameters demonstrated robust performance despite B 0 and B 1 field inhomogeneities. Phantom T 1 ρ CW and T 1 ρ Ad mapping exhibited comparable precision. In vivo experiments on healthy volunteers showed that myocardial T 1 ρ Ad is higher than T 1 ρ CW (106.1 ± 7.1 vs. 47.0 ± 5.1 ms, p &lt; 0.01) with better precision (11.4% ± 2.6% vs. 14.5% ± 2.1%, p &lt; 0.01) and less spatial variation (10.9% ± 3.0% vs. 14.4% ± 3.4%, p &lt; 0.01). Both T 1 ρ CW and T 1 ρ Ad mapping agreed with late gadolinium enhancement findings in the porcine model and the patient, and exhibited improved contrast compared to T 1 and T 2 mapping. Conclusion Both T 1 ρ CW and T 1 ρ Ad are promising for non‐contrast detection of various cardiomyopathies at 0.55 T, but T 1 ρ Ad demonstrates better spatial uniformity than T 1 ρ CW .","journal":"Magnetic Resonance in Medicine","year":2025,"id":539480,"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":3,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9579,"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":1426560,"name":"Michael Crabb","orcid":"0000-0002-7919-9731","position":1,"is_corresponding":false},{"id":1426561,"name":"Simon Littlewood","orcid":"0000-0001-7433-1409","position":2,"is_corresponding":false},{"id":1425688,"name":"Karl Kunze","orcid":"0000-0001-6403-2712","position":3,"is_corresponding":false},{"id":842314,"name":"Juliet Varghese","orcid":"0000-0003-2882-2893","position":4,"is_corresponding":false},{"id":770621,"name":"Katherine Binzel","orcid":"0000-0001-9439-3367","position":5,"is_corresponding":false},{"id":456368,"name":"Mahmood Khan","orcid":"0000-0003-3430-374X","position":6,"is_corresponding":false},{"id":307116,"name":"Orlando P. Simonetti","orcid":"0000-0002-8994-0095","position":7,"is_corresponding":false},{"id":1135847,"name":"Claudia Prieto","orcid":"0000-0003-4602-2523","position":8,"is_corresponding":false},{"id":1426562,"name":"René M. Botnar","orcid":"0000-0003-2811-2509","position":9,"is_corresponding":false},{"id":1426559,"name":"Dongyue Si","orcid":"0000-0002-0080-3052","position":0,"is_corresponding":true}],"reference_count":32,"raw_metadata":null,"created_at":"2026-07-19T02:52:30.048313Z","pmid":"40391626","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":[]}