{"doi":"10.1002/mrm.30420","title":"<scp>Three‐dimensional</scp> high‐isotropic‐resolution <scp>MR fingerprinting</scp> optimized for 0. <scp>55 T</scp>","abstract":"Abstract Purpose To provide a fast quantitative imaging approach for a 0.55T scanner, where signal‐to‐noise ratio is limited by the field strength and k‐space sampling speed is limited by a lower specification gradient system. Methods We adapted the three‐dimensional spiral projection imaging MR fingerprinting approach to 0.55T scanners, with additional features incorporated to improve the image quality of quantitative brain and musculoskeletal imaging, including (i) improved k‐space sampling efficiency, (ii) Cramér‐Rao lower bound optimized flip‐angle pattern for specified T 1 and T 2 at 0.55 T, (iii) gradient trajectory correction, (iv) attention‐based denoising, and (v) motion estimation and correction. Results The proposed MRF acquisition and reconstruction framework can provide high‐quality 1.2‐mm isotropic whole‐brain quantitative maps and 1‐mm isotropic knee quantitative maps, each acquired in 4.5 min. The proposed method was validated in both phantom and in vivo brain and knee studies. Conclusion By proposing novel methods and integrating advanced techniques, we achieved high‐isotropic‐resolution MRF on a 0.55T scanner, demonstrating enhanced efficiency, motion resilience, and quantitative accuracy.","journal":"Magnetic Resonance in Medicine","year":2025,"id":516898,"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":8,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9386,"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":263265,"name":"Congyu Liao","orcid":"0000-0003-2270-276X","position":1,"is_corresponding":false},{"id":1382794,"name":"Zheren Zhu","orcid":"0000-0002-8378-5063","position":2,"is_corresponding":false},{"id":406892,"name":"Zhitao Li","orcid":"0000-0003-3462-6549","position":3,"is_corresponding":false},{"id":1061441,"name":"Rupsa Bhattacharjee","orcid":"0000-0001-9893-3527","position":4,"is_corresponding":false},{"id":1383258,"name":"Mark Nishmura","orcid":null,"position":5,"is_corresponding":false},{"id":864196,"name":"Zhixing Wang","orcid":"0000-0002-8189-0601","position":6,"is_corresponding":false},{"id":383637,"name":"Nan Wang","orcid":"0000-0002-7616-8083","position":7,"is_corresponding":false},{"id":864197,"name":"Zihan Zhou","orcid":"0000-0002-9034-0951","position":8,"is_corresponding":false},{"id":302955,"name":"Quan Chen","orcid":"0000-0001-5570-2462","position":9,"is_corresponding":false},{"id":1196936,"name":"Daniel P. Abraham","orcid":"0000-0003-0402-9620","position":10,"is_corresponding":false},{"id":32193,"name":"Sharmila Majumdar","orcid":"0000-0002-0201-871X","position":11,"is_corresponding":false},{"id":263969,"name":"Javier Villanueva‐Meyer","orcid":"0000-0002-1212-4233","position":12,"is_corresponding":false},{"id":106869,"name":"Yang Yang","orcid":"0000-0002-2841-4243","position":13,"is_corresponding":false},{"id":263269,"name":"Kawin Setsompop","orcid":"0000-0003-0455-7634","position":14,"is_corresponding":false},{"id":656688,"name":"Xiaozhi Cao","orcid":"0000-0001-5095-648X","position":0,"is_corresponding":true}],"reference_count":61,"raw_metadata":null,"created_at":"2026-07-19T02:48:54.768083Z","pmid":"39815710","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":[]}