{"doi":"10.1117/12.2653119","title":"2D versus 3D comparison of angiographic imaging biomarkers using computational fluid dynamics simulations of contrast injections","abstract":"Quantitative angiography (QAngio) may provide hemodynamic information during neurointerventional procedures through imaging biomarkers related to contrast flow. The standard clinical implementation of QAngio is limited by projection imaging: analysis of contrast motion within complex 3D geometries is restricted to 1-2 projection views, truncating the potential wealth of imaging biomarkers related to disease progression or efficacy of treatment. To understand the limitations of 2D biomarkers, we propose the use of in-silico contrast distributions to investigate the potential benefits of 3D-QAngio within the context of neurovascular hemodynamics. Ground-truth in-silico contrast distributions were generated in two patient-specific intracranial aneurysm models, accounting for the physical interactions of contrast media and blood. A short bolus of contrast was utilized to obtain full a wash-in/ wash-out cycle within the aneurysm ROI. Simulated angiograms mimicking clinical cone-beam CT (CBCT) acquisitions were then generated, and volumetric contrast distributions were reconstructed to analyze bulk contrast flow. The ground-truth 3D-CFD, reconstructed 3D-CBCT-DSA, and 2D-DSA projections were used to extract QAngio parameters related to contrast time dilution curves, such as area under the curve (AUC), peak height (PH), mean-transit-time (MTT), time-to-peak (TTP), and time to arrival (TTA). An initial comparison of quantitative flow parameters in both 2D and 3D, in a smaller and larger aneurysm, indicated that 3D-QAngio can provide a good description of bulk flow characteristics (TTA, TTP, MTT), but recovery of integral parameters (PH, AUC) aneurysms is limited. Nonetheless, incorporation of 3D-QAngio methods may provide additional insight into our understanding of abnormal vascular flow patterns.","journal":"PubMed","year":2023,"id":392296,"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":5,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9538,"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":346865,"name":"Mohammad Mahdi Shiraz Bhurwani","orcid":"0000-0001-6226-4392","position":1,"is_corresponding":false},{"id":417678,"name":"Kyle Williams","orcid":"0000-0003-4333-9111","position":2,"is_corresponding":false},{"id":774800,"name":"Venkat Keshav Chivukula","orcid":"0000-0002-8457-8509","position":3,"is_corresponding":false},{"id":437471,"name":"Daniel R. Bednarek","orcid":"0000-0002-6096-3568","position":4,"is_corresponding":false},{"id":437472,"name":"Stephen Rudin","orcid":"0000-0002-2481-7520","position":5,"is_corresponding":false},{"id":333750,"name":"Ciprian N. Ionita","orcid":"0000-0001-7049-0592","position":6,"is_corresponding":false},{"id":1132262,"name":"Allison Shields","orcid":null,"position":0,"is_corresponding":true}],"reference_count":20,"raw_metadata":null,"created_at":"2026-07-19T01:18:52.232318Z","pmid":"37424835","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":[]}