{"doi":"10.1002/mrm.70215","title":"Jointly Learned <scp>3D</scp> Non‐Cartesian Sampling With Wave Encoding and Reconstruction for Neurovascular Phase Contrast <scp>MRI</scp>","abstract":"ABSTRACT Purpose To develop accelerated 3D phase contrast (PC) MRI using jointly learned wave encoding and reconstruction. Methods Pseudo‐fully sampled neurovascular 4D flow data ( N = 40) and a simulation framework were used to learn phase encoding locations, wave readout parameters, and model‐based reconstruction network (MoDL) for a rapid 3D PC scan (2.25 min). Parameters were also learned for an otherwise identical scan without wave encoding. Prospective scans with and without wave sampling, time‐matched 3D radial, and reference 3D radial (5.65 min) were conducted in a flow phantom and 12 healthy participants. Flow rate, pixel‐wise velocity, and variability of maximum velocity () were compared. Results In the phantom, learned wave scans provided accurate flow rates compared to flow probe values (0.170 ± 0.002 vs. 0.17, 0.152 ± 0.003 vs. 0.15, 1.838 ± 0.044 vs. 1.83 L/min) and showed high correlation with reference scan (slope = 0.97, R 2 = 0.99). In vivo, learned wave scans demonstrated reduced aliasing and blurring, and better small vessel conspicuity compared to scans without wave sampling and time‐matched 3D radial scans. The internal carotid artery (ICA) flow rate coefficient of variation (CV) and intraclass correlation coefficient (ICC) for learned wave scans were similar to reference 3D radial scans (CV = 6.569, ICC = 0.927; reference CV = 6.553, ICC = 0.910). Learned wave sampling demonstrated similar or lower in middle cerebral artery (MCA), basilar artery (BA), superior sagittal sinus (SSS), and most ICA segments than the longer reference scan. Conclusion This work demonstrates feasibility, improved image quality and accurate flow measurements of learned wave sampling and MoDL reconstruction for 3D PC MRI.","journal":"Magnetic Resonance in Medicine","year":2025,"id":584554,"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":0.916,"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":1443519,"name":"Brock Jolicoeur","orcid":"0009-0002-9354-7103","position":1,"is_corresponding":false},{"id":911451,"name":"James A. Rice","orcid":"0000-0002-2029-6705","position":2,"is_corresponding":false},{"id":1497719,"name":"Caroline A. Doctor","orcid":"0009-0006-5989-1401","position":3,"is_corresponding":false},{"id":1444031,"name":"Zaynab S. Yardim","orcid":null,"position":4,"is_corresponding":false},{"id":389939,"name":"Leonardo A. Rivera‐Rivera","orcid":"0000-0001-5629-8721","position":5,"is_corresponding":false},{"id":389938,"name":"Laura Eisenmenger","orcid":"0000-0001-6764-4239","position":6,"is_corresponding":false},{"id":313994,"name":"Kevin M. Johnson","orcid":"0000-0002-8660-0853","position":7,"is_corresponding":false},{"id":1064721,"name":"Chenwei Tang","orcid":"0000-0001-8140-0548","position":0,"is_corresponding":true}],"reference_count":44,"raw_metadata":null,"created_at":"2026-07-19T02:59:16.166424Z","pmid":"41359402","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":[]}