{"doi":"10.1148/ryct.220221","title":"Mapping the Spatial Extent of Hypoperfusion in Chronic Thromboembolic Pulmonary Hypertension Using Multienergy CT","abstract":"Purpose: To assess if a novel automated method to spatially delineate and quantify the extent of hypoperfusion on multienergy CT angiograms can aid the evaluation of chronic thromboembolic pulmonary hypertension (CTEPH) disease severity. Materials and Methods: Multienergy CT angiograms obtained between January 2018 and December 2020 in 51 patients with CTEPH (mean age, 47 years ± 17 [SD]; 27 women) were retrospectively compared with those in 110 controls with no imaging findings suggestive of pulmonary vascular abnormalities (mean age, 51 years ± 16; 81 women). Parenchymal iodine values were automatically isolated using deep learning lobar lung segmentations. Low iodine concentration was used to delineate areas of hypoperfusion and calculate hypoperfused lung volume (HLV). Receiver operating characteristic curves, correlations with preoperative and postoperative changes in invasive hemodynamics, and comparison with visual assessment of lobar hypoperfusion by two expert readers were evaluated. Results: = 0.67 for reader 2). Conclusion: CT-Spectral Imaging (Multienergy), Pulmonary, Pulmonary Arteries, Embolism/Thrombosis, Chronic Thromboembolic Pulmonary Hypertension, Multienergy CT, Hypoperfusion© RSNA, 2023.","journal":"Radiology Cardiothoracic Imaging","year":2023,"id":354092,"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":7,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.509,"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":677223,"name":"Kyle Hasenstab","orcid":"0000-0002-4687-960X","position":1,"is_corresponding":false},{"id":656490,"name":"Nick H. Kim","orcid":"0000-0003-4702-650X","position":2,"is_corresponding":false},{"id":528663,"name":"Michael M. Madani","orcid":"0000-0001-8056-8007","position":3,"is_corresponding":false},{"id":270458,"name":"Atul Malhotra","orcid":"0000-0002-9509-1827","position":4,"is_corresponding":false},{"id":715763,"name":"Lewis D. Hahn","orcid":"0000-0003-0468-9442","position":5,"is_corresponding":false},{"id":285682,"name":"Seth Kligerman","orcid":"0000-0002-1532-1371","position":6,"is_corresponding":false},{"id":365032,"name":"Albert Hsiao","orcid":"0000-0002-9412-1369","position":7,"is_corresponding":false},{"id":365029,"name":"Francisco Contijoch","orcid":"0000-0001-9616-3274","position":8,"is_corresponding":false},{"id":1101165,"name":"Elizabeth Bird","orcid":"0000-0003-4545-0497","position":0,"is_corresponding":true}],"reference_count":33,"raw_metadata":null,"created_at":"2026-07-19T01:13:06.959762Z","pmid":"37693197","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":[]}