{"doi":"10.1088/1361-6560/ace0f1","title":"Determining the effect of cardiac blood volume on accuracy of uptake rate constants by simulation","abstract":"Abstract Objective . There is great interest in better understanding coronary microvascular disease using mouse models. Typical quantification requires dynamic imaging to estimate the rate constant K 1 of the tracer moving from the blood into the myocardium. In addition to K 1 , it is also desirable to determine blood volume fraction V , which if known allows for more accurate fitting of K 1 . Our previously published kinetic modeling software did not consider the effect of V . To ensure a better fit of experimental data to the model for myocardial <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" overflow=\"scroll\"> <mml:mi>μ</mml:mi> </mml:math> SPECT imaging, in this work we updated our kinetic modeling software to include a blood volume fraction V , which adds a fraction of the arterial activity concentration into the tissue concentration. Approach . The tissue and blood time-activity curves (TACs) used for fit input were generated using ideal equations with known values in MATLAB. This allowed post-fit results to be compared to known values to determine fit errors. Parameters that were varied in generating the TACs included blood volume fraction (0, 0.05, 0.1, 0.2 and 0.3), K 1 (0.5, 1.5, 2.5 ml min −1 g −1 ), frame length (1, 2, 5, 10, 15, 20 s), FWHM of the input Gaussian (10, 20, 40 s), and time of the injection peak relative to frame duration. Blood volume-fraction results have low error when blood volume is lowest, but results worsen as frame length and K 1 increase. Main results . We demonstrated that blood volume can be accurately determined, and also show how fit accuracy varies across TACs with different input properties. Significance . This information allows for robust use of the fitting algorithm and aids in understanding fit performance when used in animal studies.","journal":"Physics in Medicine and Biology","year":2023,"id":388410,"datarank":0.10397207708399181,"base_score":0.6931471805599453,"endowment":0.6931471805599453,"self_citation_contribution":0.10397207708399181,"citation_network_contribution":0.0,"self_endowment_contribution":0.10397207708399181,"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":0.9499,"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":332198,"name":"Marie Guerraty","orcid":"0000-0002-0766-1253","position":1,"is_corresponding":false},{"id":1158376,"name":"S.C. Moore","orcid":null,"position":2,"is_corresponding":false},{"id":659612,"name":"Scott D. Metzler","orcid":"0000-0001-9935-0024","position":3,"is_corresponding":false},{"id":1144041,"name":"Lindsay C. Johnson","orcid":null,"position":0,"is_corresponding":true}],"reference_count":11,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T01:18:18.214733Z","pmid":"37348483","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":[]}