{"doi":"10.1117/1.jmi.10.4.043501","title":"Ex vivo coronary calcium volume quantification using a high-spatial-resolution clinical photon-counting-detector computed tomography","abstract":"PurposeCoronary artery calcification (CAC) is an important indicator of coronary disease. Accurate volume quantification of CAC is challenging using computed tomography (CT) due to calcium blooming, which is a consequence of limited spatial resolution. Ex vivo coronary specimens were scanned on an ultra-high-resolution (UHR) clinical photon-counting detector (PCD) CT scanner, and the accuracy of CAC volume estimation was compared with a state-of-the-art conventional energy-integrating detector (EID) CT, a previous-generation investigational PCD-CT, and micro-CT.ApproachCAC specimens (n = 13) were scanned on EID-CT and PCD-CT using matched parameters (120 kV, 9.3 mGy CTDIvol). EID-CT images were reconstructed using our institutional routine clinical protocol for CAC quantification. UHR PCD-CT data were reconstructed using a sharper kernel. An image-based denoising algorithm was applied to the PCD-CT images to achieve similar noise levels as EID-CT. Micro-CT images served as the volume reference standard. Calcification images were segmented, and their volume estimates were compared. The CT data were further compared with previous work using an investigational PCD-CT.ResultsCompared with micro-CT, CT volume estimates had a mean absolute percent error of 24.1 % ± 25.6 % for clinical PCD-CT, 60.1 % ± 48.2 % for EID-CT, and 51.1 % ± 41.7 % for previous-generation PCD-CT. Clinical PCD-CT absolute percent error was significantly (p < 0.01) lower than both EID-CT and previous generation PCD-CT. The mean calcification CT number and contrast-to-noise ratio were both significantly (p < 0.01) higher in clinical PCD-CT relative to EID-CT.ConclusionsUHR clinical PCD-CT showed reduced calcium blooming artifacts and further enabled improved accuracy of CAC quantification beyond that of conventional EID-CT and previous generation PCD-CT systems.","journal":"Journal of Medical Imaging","year":2023,"id":341297,"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":15,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.8995,"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":1077317,"name":"Patrick D. VanMeter","orcid":null,"position":1,"is_corresponding":false},{"id":254231,"name":"Kishore Rajendran","orcid":"0000-0002-8314-3642","position":2,"is_corresponding":false},{"id":237063,"name":"Shuai Leng","orcid":"0000-0002-6453-9481","position":3,"is_corresponding":false},{"id":237065,"name":"Cynthia H. McCollough","orcid":"0000-0002-5346-332X","position":4,"is_corresponding":false},{"id":511067,"name":"Jeffrey F. Marsh","orcid":null,"position":0,"is_corresponding":true}],"reference_count":50,"raw_metadata":null,"created_at":"2026-07-19T01:11:03.393978Z","pmid":"37408984","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":[]}