{"doi":"10.1117/1.jmi.12.6.063501","title":"Soft-tissue lesion and microcalcification detectability in cone-beam breast CT: cascaded system analysis","abstract":"PurposeWe aim to investigate the performance of dedicated breast computed tomography (CT) for the detection of soft-tissue lesions and compare it to the detection of microcalcification clusters using cascaded systems analysis, with the intent of identifying which lesion type should be used for system optimization.ApproachSignal and noise were propagated through the imaging chain using a cascaded systems model to obtain the modulation transfer function and noise power spectrum. Two imaging tasks were considered: a soft-tissue mass lesion modeled as a disk of 4 mm diameter and a cluster of microcalcifications modeled as calcium carbonate spheres of 220 μm diameter. Detectability indices using three numerical observer models were obtained for various scintillator thicknesses and acquisition conditions at a fixed 4.5 mGy mean glandular dose.ResultsDetectability index trends are reversed between soft-tissue lesion and microcalcification cluster for the range of X-ray tube voltages and filtrations studied, indicating a potential need for compromise. However, for each of the 150 combinations studied (6 kV settings × 5 Cu filter thicknesses × 5 CsI:Tl scintillator thicknesses) and for each of the three numerical observer models, the detectability index for soft-tissue lesions always exceeded the microcalcification cluster.ConclusionWhen the lesion type is unknown, such as during breast cancer screening, it is more appropriate to optimize the system parameters for the task of detecting a microcalcification cluster, as the detectability index for the soft-tissue lesion exceeded that for the microcalcification cluster for all conditions investigated.","journal":"Journal of Medical Imaging","year":2025,"id":585637,"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":0.9657,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":376899,"name":"Hsin Wu Tseng","orcid":"0000-0002-2892-1288","position":1,"is_corresponding":false},{"id":933097,"name":"Jing‐Tzyh Alan Chiang","orcid":"0000-0003-4882-0406","position":2,"is_corresponding":false},{"id":376900,"name":"Srinivasan Vedantham","orcid":"0000-0003-4123-8392","position":3,"is_corresponding":false},{"id":1344501,"name":"Thomas C. Larsen","orcid":null,"position":0,"is_corresponding":true}],"reference_count":0,"raw_metadata":null,"created_at":"2026-07-19T02:59:24.273134Z","pmid":"41404593","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":[]}