{"doi":"10.1002/mrm.30083","title":"Inline automatic quality control of <scp>2D</scp> phase‐contrast flow <scp>MRI</scp> for subject‐specific scan time adaptation","abstract":"PURPOSE: To develop an inline automatic quality control to achieve consistent diagnostic image quality with subject-specific scan time, and to demonstrate this method for 2D phase-contrast flow MRI to reach a predetermined SNR. METHODS: We designed a closed-loop feedback framework between image reconstruction and data acquisition to intermittently check SNR (every 20 s) and automatically stop the acquisition when a target SNR is achieved. A free-breathing 2D pseudo-golden-angle spiral phase-contrast sequence was modified to listen for image-quality messages from the reconstructions. Ten healthy volunteers and 1 patient were imaged at 0.55 T. Target SNR was selected based on retrospective analysis of cardiac output error, and performance of the automatic SNR-driven \"stop\" was assessed inline. RESULTS: SNR calculation and automated segmentation was feasible within 20 s with inline deployment. The SNR-driven acquisition time was 2 min 39 s ± 67 s (aorta) and 3 min ± 80 s (main pulmonary artery) with a min/max acquisition time of 1 min 43 s/4 min 52 s (aorta) and 1 min 43 s/5 min 50 s (main pulmonary artery) across 6 healthy volunteers, while ensuring a diagnostic measurement with relative absolute error in quantitative flow measurement lower than 2.1% (aorta) and 6.3% (main pulmonary artery). CONCLUSION: The inline quality control enables subject-specific optimized scan times while ensuring consistent diagnostic image quality. The distribution of automated stopping times across the population revealed the value of a subject-specific scan time.","journal":"Magnetic Resonance in Medicine","year":2024,"id":450017,"datarank":0.269363320014002,"base_score":1.6094379124341003,"endowment":1.6094379124341003,"self_citation_contribution":0.24141568686511508,"citation_network_contribution":0.027947633148886927,"self_endowment_contribution":0.24141568686511508,"citer_contribution":0.027947633148886927,"corpus_percentile":null,"corpus_rank":null,"citation_count":4,"citer_count":3,"citers_with_citation_signal":2,"citers_with_endowment":2,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9509,"is_data_producer":true,"deposit_databanks":{"ClinicalTrials.gov":["NCT03331380"]},"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":329135,"name":"Rajiv Ramasawmy","orcid":"0000-0002-9821-9187","position":1,"is_corresponding":false},{"id":512515,"name":"Ahsan Javed","orcid":"0000-0003-1311-1247","position":2,"is_corresponding":false},{"id":294247,"name":"Robert J. Lederman","orcid":"0000-0003-1202-6673","position":3,"is_corresponding":false},{"id":342550,"name":"Kelvin Chow","orcid":"0000-0003-0698-1746","position":4,"is_corresponding":false},{"id":280607,"name":"Adrienne Campbell‐Washburn","orcid":"0000-0002-7169-5693","position":5,"is_corresponding":false},{"id":1270256,"name":"Pierre Daudé","orcid":"0000-0002-0565-8974","position":0,"is_corresponding":true}],"reference_count":27,"raw_metadata":null,"created_at":"2026-07-19T02:02:25.004119Z","pmid":"38469944","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":[]}