{"doi":"10.1002/mrm.70199","title":"Motion Corrected Subspace Reconstruction With Navigators for Quantitative High‐Resolution Spiral First‐Pass Myocardial Perfusion Imaging at 3 Tesla","abstract":"PURPOSE: To optimize a spiral acquisition using a fixed-angle subspace navigator and golden angle trajectory, combined with motion-corrected (MOCO) subspace reconstruction, for motion-robust, quantitative high-resolution whole-heart first-pass perfusion imaging at 3 T. METHODS: . Non-rigid deformation fields estimated from auxiliary images were incorporated into the subspace reconstruction. Performance was tested in XCAT simulations and 5 retrospectively undersampled free-breathing datasets. Prospective reconstructions using Subspace, L1-SENSE, and SENSE with and without MOCO from 22 patients were visually graded (1-5) by three cardiovascular imagers. In 11 of these patients an arterial input function (AIF) was acquired and myocardial blood flow (MBF) maps were calculated using Fermi-function deconvolution. RESULTS: In simulations and retrospective datasets, the navigator captured cardiac motion and contrast dynamics effectively, and the (1 + 7)-arm k-t GA DD1-Hs configuration improved subspace reconstruction through increased spatiotemporal incoherency. In prospective studies, Subspace-MOCO achieved the highest visual scores (p < 0.001), with MOCO improving all methods (EMM ± 1.96 × SE: SENSE-MOCO 2.2 ± 0.2, L1-SENSE-MOCO 4.1 ± 0.2, Subspace-MOCO 4.6 ± 0.12). Subspace-MOCO also provided superior boundary sharpness and global edge sharpness, higher NCC and NMI (p < 0.05). Its MBF values were similar to those obtained with SENSE-MOCO and closer than those reconstructed from L1-SENSE-MOCO. CONCLUSIONS: The proposed navigator-guided MOCO subspace reconstruction substantially reduces respiratory motion artifacts and enables high-resolution, motion-corrected whole-heart quantitative spiral perfusion imaging.","journal":"Magnetic Resonance in Medicine","year":2025,"id":585338,"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":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.943,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"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":712885,"name":"Junyu Wang","orcid":"0000-0001-8314-4525","position":1,"is_corresponding":false},{"id":1131839,"name":"Xitong Wang","orcid":"0009-0002-8457-9189","position":2,"is_corresponding":false},{"id":970252,"name":"Shen Zhao","orcid":"0000-0003-1736-6378","position":3,"is_corresponding":false},{"id":238112,"name":"Sizhuo Liu","orcid":"0000-0002-3248-0737","position":4,"is_corresponding":false},{"id":523411,"name":"Yang Yang","orcid":"0000-0001-8833-7641","position":5,"is_corresponding":false},{"id":386184,"name":"Yoojin Lee","orcid":"0000-0003-2475-5857","position":6,"is_corresponding":false},{"id":1101268,"name":"Christopher Lee","orcid":"0000-0002-9017-819X","position":7,"is_corresponding":false},{"id":225213,"name":"Michael Salerno","orcid":"0000-0001-7051-1031","position":8,"is_corresponding":false},{"id":1085047,"name":"Quan Chen","orcid":"0000-0002-5392-8000","position":0,"is_corresponding":true}],"reference_count":47,"raw_metadata":null,"created_at":"2026-07-19T02:59:20.067334Z","pmid":"41399119","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":[]}