{"doi":"10.1002/mrm.29672","title":"Rapid <scp>3D T<sub>1</sub></scp> mapping using deep <scp>learning‐assisted Look‐Locker</scp> inversion recovery <scp>MRI</scp>","abstract":"Purpose Conventional 3D Look‐Locker inversion recovery (LLIR) T 1 mapping requires multi‐repetition data acquisition to reconstruct images at different inversion times for T 1 fitting. To ensure B 1 robustness, sufficient time of delay (TD) is needed between repetitions, which prolongs scan time. This work proposes a novel deep learning‐assisted LLIR MRI approach for rapid 3D T 1 mapping without TD. Theory and Methods The proposed approach is based on the fact that , the effective T 1 in LLIR imaging, is independent of TD and can be estimated from both LLIR imaging with and without TD, while accurate conversion of to T 1 requires TD. Therefore, deep learning can be used to learn the conversion of to T 1 , which eliminates the need for TD. This idea was implemented for inversion‐recovery‐prepared Golden‐angel RAdial Sparse Parallel T 1 mapping (GraspT 1 ). 39 GraspT 1 datasets with a TD of 6 s (GraspT 1 ‐TD6) were used for training, which also incorporates additional anatomical images. The trained network was applied for T 1 estimation in 14 GraspT 1 datasets without TD (GraspT 1 ‐TD0). The robustness of the trained network was also tested. Results Deep learning‐based T 1 estimation from GraspT 1 ‐TD0 is accurate compared to the reference. Incorporation of additional anatomical images improves the accuracy of T 1 estimation. The technique is also robust against slight variation in spatial resolution, imaging orientation and scanner platform. Conclusion Our approach eliminates the need for TD in 3D LLIR imaging without affecting the T 1 estimation accuracy. It represents a novel use of deep learning towards more efficient and robust 3D LLIR T 1 mapping.","journal":"Magnetic Resonance in Medicine","year":2023,"id":363877,"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":7,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9503,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":713273,"name":"Ding Xia","orcid":"0000-0002-1396-4082","position":1,"is_corresponding":false},{"id":772725,"name":"Xiang Xu","orcid":"0000-0002-3437-1470","position":2,"is_corresponding":false},{"id":106869,"name":"Yang Yang","orcid":"0000-0002-2841-4243","position":3,"is_corresponding":false},{"id":576970,"name":"Yao Wang","orcid":"0000-0003-3199-3802","position":4,"is_corresponding":false},{"id":106866,"name":"Fang Liu","orcid":"0000-0001-8032-6681","position":5,"is_corresponding":false},{"id":404811,"name":"Li Feng","orcid":"0000-0002-8692-7645","position":6,"is_corresponding":false},{"id":1118303,"name":"Haoyang Pei","orcid":"0009-0007-9258-673X","position":0,"is_corresponding":true}],"reference_count":25,"raw_metadata":null,"created_at":"2026-07-19T01:14:32.510714Z","pmid":"37125662","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":[]}