{"doi":"10.1002/mrm.28636","title":"Dynamic oxygen‐17 MRI with adaptive temporal resolution using golden‐means‐based 3D radial sampling","abstract":"Purpose The aim of this study was to develop a high‐resolution 3D oxygen‐17 ( 17 O) MRI method to delineate the kinetics of 17 O‐enriched water (H 2 17 O) across the entire mouse brain after a bolus injection via the tail vein. Methods The dynamic 17 O signal was acquired with a golden‐means‐based 3D radial sampling scheme. To achieve adequate temporal resolution with preserved spatial resolution, a k ‐space–weighted view sharing strategy was used in image reconstruction with an adaptive window size tailored to the kinetics of the 17 O signal. Simulation studies were performed to determine the adequate image reconstruction parameters. The established method was applied to delineating the kinetics of intravenously injected H 2 17 O in vivo in the post‐stroke mouse brain. Results The proposed dynamic 17 O‐MRI method achieved an isotropic resolution of 1.21 mm (0.77 mm nominal) in mouse brain at 9.4T, with the temporal resolution increased gradually from 3 s at the initial phase of rapid signal increase to 15 s at the steady‐state. The high spatial resolution enabled the delineation of the heterogeneous H 2 17 O uptake and washout kinetics in stroke‐affected mouse brain. Conclusion The current study demonstrated a 3D 17 O‐MRI method for dynamic monitoring of 17 O signal changes with high spatial and temporal resolution. The method can be utilized to quantify physiological parameters such as cerebral blood flow and blood–brain barrier permeability by tracking injected H 2 17 O. It can also be used to measure oxygen consumption rate in 17 O‐oxygen inhalation studies.","journal":"Magnetic Resonance in Medicine","year":2020,"id":117131,"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":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.923,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":547271,"name":"Huiyun Gao","orcid":null,"position":1,"is_corresponding":false},{"id":379446,"name":"Ki-Hwan Kim","orcid":"0000-0002-2882-4816","position":2,"is_corresponding":false},{"id":379445,"name":"Yuchi Liu","orcid":"0000-0003-3533-1697","position":3,"is_corresponding":false},{"id":541227,"name":"Ciro Ramos-Estébanez","orcid":"0000-0001-6737-5374","position":4,"is_corresponding":false},{"id":546659,"name":"Yu Luo","orcid":"0000-0002-7939-5505","position":5,"is_corresponding":false},{"id":547272,"name":"Yunmei Wang","orcid":null,"position":6,"is_corresponding":false},{"id":379451,"name":"Xin Yu","orcid":"0000-0001-8996-0267","position":7,"is_corresponding":false},{"id":379444,"name":"Yuning Gu","orcid":"0009-0000-5588-3072","position":0,"is_corresponding":true}],"reference_count":51,"raw_metadata":null,"created_at":"2026-07-18T23:13:51.309289Z","pmid":"33368649","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":[]}