{"doi":"10.1117/12.2654308","title":"Geometrically-independent contrast dilution gradient (CDG) velocimetry using photon-counting 1000 fps high speed angiography (HSA) for 2D velocity distribution estimation","abstract":"<strong>Purpose:</strong> Previous studies have demonstrated the efficacy of contrast dilution gradient (CDG) analysis in determining large vessel velocity distributions from 1000 fps high-speed angiography (HSA). However, the method required vessel centerline extraction, which made it applicable only to non-tortuous geometries using a highly specific contrast injection technique. This study seeks to remove the need for a priori knowledge regarding the direction of flow and modify the vessel sampling method to make the algorithm more robust to non-linear geometries. <strong>Materials and Methods:</strong> 1000 fps HSA acquisitions were obtained in vitro with a benchtop flow loop using the XCActaeon (Varex Inc.) photon-counting detector, and in silico using a passive-scalar transport model within a computational fluid dynamics (CFD) simulation. CDG analyses were obtained using gridline sampling across the vessel, and subsequent 1D velocity measurement in both the x- and y-directions. The velocity magnitudes derived from the component CDG velocity vectors were aligned with CFD results via co-registration of the resulting velocity maps and compared using mean absolute percent error (MAPE) between pixels values in each method after temporal averaging of the 1-ms velocity distributions. <strong>Results:</strong> Regions well-saturated with contrast throughout the acquisition showed agreement when compared to CFD (MAPE of 18% for the carotid bifurcation inlet and MAPE of 27% for the internal carotid aneurysm), with respective completion times of 137 seconds and 5.8 seconds. <strong>Conclusions:</strong> CDG may be used to obtain velocity distributions in and surrounding vascular pathologies provided the contrast injection is sufficient to provide a gradient, and diffusion of contrast through the system is negligible.","journal":"PubMed","year":2023,"id":406294,"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":0.9614,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1132262,"name":"Allison Shields","orcid":null,"position":1,"is_corresponding":false},{"id":437470,"name":"Swetadri Vasan Setlur Nagesh","orcid":"0000-0001-6466-5368","position":2,"is_corresponding":false},{"id":437471,"name":"Daniel R. Bednarek","orcid":"0000-0002-6096-3568","position":3,"is_corresponding":false},{"id":437472,"name":"Stephen Rudin","orcid":"0000-0002-2481-7520","position":4,"is_corresponding":false},{"id":333750,"name":"Ciprian N. Ionita","orcid":"0000-0001-7049-0592","position":5,"is_corresponding":false},{"id":417678,"name":"Kyle Williams","orcid":"0000-0003-4333-9111","position":0,"is_corresponding":true}],"reference_count":9,"raw_metadata":null,"created_at":"2026-07-19T01:21:03.432924Z","pmid":"37425073","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":[]}