Journal of cardiovascular computed tomography最新文献

{"title":"Comparison of dual-energy iodine and standard subtraction methods for myocardial extracellular volume quantification using cardiac computed tomography.","authors":"Daniel A Lorenzatti, Annalisa Filtz, Pamela Piña, Jeirym Miranda, Siddarth Ragupathi, Felipe Contreras Yametti, Andrea Scotti, Winsome Elliott-Bailey, João L Cavalcante, Thomas Ivanc, Azeem Latib, Mario J Garcia, Leandro Slipczuk","doi":"10.1016/j.jcct.2025.03.009","DOIUrl":"https://doi.org/10.1016/j.jcct.2025.03.009","url":null,"abstract":"<p><strong>Background: </strong>Computed tomography (CT)-derived extracellular volume fraction (ECV) quantifies myocardial fibrosis noninvasively, comparable to cardiovascular magnetic resonance. The conventional subtraction method (ECV-conv) requires dedicated pre- and post-contrast acquisitions, while the dual-energy spectral method (ECV-spec) uses only the post-contrast phase. We aimed to compare these methods in patients undergoing CT planning for transcatheter aortic valve replacement (TAVR).</p><p><strong>Methods: </strong>We prospectively included patients undergoing CT-TAVR evaluation on a dual-energy dual-layer detector scanner. Baseline pre-contrast and equilibrium delay phase (at 5 ​min) prospectively ECG-triggered acquisitions were co-registered for ECV-conv calculation. Equilibrium phase spectral iodine maps were derived for ECV-spec measurement and compared with ECV-con maps using a mid-septal and a global mid-ventricular region of interest.</p><p><strong>Results: </strong>Overall, 78 patients (53 ​% female, mean age 77 years) were analyzed. There was a minor overestimation in global measurement by ECV-spec (31.8 ​± ​4.6 ​% vs 30.3 ​± ​5.3 ​%, p ​= ​0.023. However, there were no significant differences between both methods for the mid-septal measurement (ECV-conv ​= ​30.4 ​± ​5.3 ​% vs ECV-spec ​= ​31.0 ​± ​5.6 ​%, p ​= ​0.196).). Both methods demonstrated comparable 95 ​% limits of agreement, with a strong correlation for mid-septal (r ​= ​0.75, p ​< ​0.0001) and a moderate correlation for global measurements (r ​= ​0.51, p ​< ​0.0001).</p><p><strong>Conclusions: </strong>Myocardial CT-ECV estimation using dual-energy spectral maps was comparable to the conventional subtraction method without the need of a pre-contrast acquisition. This approach may help reduce imaging and processing times, as well as minimize radiation exposure.</p>","PeriodicalId":94071,"journal":{"name":"Journal of cardiovascular computed tomography","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143812929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting categories of coronary artery calcium scores from chest X-ray images using deep learning.","authors":"Youngtaek Hong, Hyunseok Jeong, Younggul Jang, Ran Heo, Seung-Ah Lee, Yeonyee E Yoon, Jina Lee, Hyung-Bok Park, Hyuk-Jae Chang","doi":"10.1016/j.jcct.2025.03.010","DOIUrl":"https://doi.org/10.1016/j.jcct.2025.03.010","url":null,"abstract":"<p><strong>Background: </strong>The coronary artery calcium (CAC) score (CACS) is recommended in clinical guidelines for coronary artery disease evaluation. However, it is being replaced by coronary computed tomography angiography as the primary diagnostic tool for patients with stable chest pain. This study aimed to develop and validate a deep learning model for predicting the CACS categories from chest X-ray radiographs (CXRs).</p><p><strong>Methods: </strong>We included 10,230 patients with available CXRs and CACSs obtained within six months. Three models were trained based on the CACS thresholds (0, 100, and 400) to distinguish zero from non-zero CACSs, CACSs of <100 and ​≥ ​100, and CACS of <400 and ​≥ ​400. The final CXR integration models incorporating clinical factors, including age, sex, and body mass index, were also trained. All models were evaluated using 10-fold cross-validation. External validation was also performed. We experimentally demonstrated the prognostic value of the predicted CACS for major adverse cardiovascular events, comparing it to the actual CACS classification.</p><p><strong>Results: </strong>The CACS classification performance of the deep learning model was promising, with areas under the curve (AUCs) of 0.74 (zero vs non-zero), 0.75 (<100 vs. ≥100), and 0.79 (<400 vs. ≥400). The accuracy of the model further improved upon the integration of clinical factors; the AUCs reached 0.77, 0.79, and 0.82, respectively, for the same CACS categories. The external validation results were consistent (AUCs of 0.78, 0.79, and 0.81, respectively).</p><p><strong>Conclusions: </strong>The deep learning model effectively classified the CACS from CXRs, especially for cases of severe calcification. This approach can cost-effectively improve coronary artery disease risk assessment and support clinical decision-making while minimizing radiation exposure.</p>","PeriodicalId":94071,"journal":{"name":"Journal of cardiovascular computed tomography","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143812945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Aortic dissection after heart transplantation for early Fontan failure: A rare complication.","authors":"Daniel O'Meara, Arene Butto, James A Kuo, Fawwaz Shaw, Ritu Sachdeva, Susan Taylor, Sassan Hashemi, Hunter C Wilson","doi":"10.1016/j.jcct.2025.03.011","DOIUrl":"https://doi.org/10.1016/j.jcct.2025.03.011","url":null,"abstract":"","PeriodicalId":94071,"journal":{"name":"Journal of cardiovascular computed tomography","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143812630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improved prediction of periprocedural myocardial injury by plaque quantification from coronary CT angiography.","authors":"Hiroki Tanisawa, Hidenari Matsumoto, Yuka Otaki, Sebastien Cadet, Satoshi Higuchi, Ryota Masaki, Kyoichi Kaneko, Kengo Suzuki, Toshitaka Okabe, Piotr J Slomka, Damini Dey, Toshiro Shinke","doi":"10.1016/j.jcct.2025.03.006","DOIUrl":"https://doi.org/10.1016/j.jcct.2025.03.006","url":null,"abstract":"","PeriodicalId":94071,"journal":{"name":"Journal of cardiovascular computed tomography","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143756821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Racial differences in atherosclerosis and coronary plaque characteristics using enhanced AI quantification (RACE-AI).","authors":"Beshoy Iskander, Natdanai Punnanithinont, Soumya Kambalapalli, Keshi Ichikawa, April Kinninger, Srikanth Krishnan, Suvasini Lakshmanan, Sion Roy, Mathew Budoff","doi":"10.1016/j.jcct.2025.03.004","DOIUrl":"https://doi.org/10.1016/j.jcct.2025.03.004","url":null,"abstract":"","PeriodicalId":94071,"journal":{"name":"Journal of cardiovascular computed tomography","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143756828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accuracy of coronary CTA using spectral CT in patients with high calcium score.","authors":"Juan Manuel Monteagudo Ruiz, Pablo Martínez-Vives, Irene Carrión-Sánchez, Cristina García-Sebastián, Eduardo Casas Rojo, Álvaro Arribas Marcos, Jose Luis Zamorano, Covadonga Fernández-Golfin","doi":"10.1016/j.jcct.2025.03.005","DOIUrl":"https://doi.org/10.1016/j.jcct.2025.03.005","url":null,"abstract":"","PeriodicalId":94071,"journal":{"name":"Journal of cardiovascular computed tomography","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143756817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Air pollution, coronary artery disease, and cardiovascular events: Insights from the PROMISE trial.","authors":"Marcel C Langenbach, Thomas Mayrhofer, Isabel L Langenbach, Michael T Lu, Julia Karady, David Maintz, Shady Abohashem, Ahmed Tawakol, Neha J Pagidipati, Svati H Shah, Maros Ferencik, Alison Motsinger-Reif, Pamela S Douglas, Borek Foldyna","doi":"10.1016/j.jcct.2025.03.001","DOIUrl":"https://doi.org/10.1016/j.jcct.2025.03.001","url":null,"abstract":"<p><strong>Background: </strong>Air pollution is associated with mortality and major adverse cardiovascular events (MACE) in the general population. However, little is known about the relationship between air pollution and coronary artery disease (CAD) and how this relates to MACE.</p><p><strong>Methods: </strong>This study utilized data from the computed tomography (CT) arm of the PROMISE trial investigating symptomatic individuals with suspected CAD. We linked levels of air pollutants (PM_2·5, PM₁₀, NO₂, and ozone) at U.S. zip codes of residence CT-derived CAD and adjudicated MACE (all-cause death, myocardial infarction, and hospitalization for unstable angina). Multivariable analyses were adjusted for the ASCVD risk score and socioeconomic determinants of health. Mediation analyses were used to test putative pathways.</p><p><strong>Results: </strong>In 4343 individuals (48 ​% males; age: 61 ​± ​8 years), elevated exposures to PM_2.5 (≥9.4 ​μg/m³) and NO₂ (≥5.3 ​ppb) were independently associated with obstructive CAD (aOR ​= ​1.23, 95%CI: 1.03-1.48, p ​= ​0.024; aOR ​= ​1.56, 95%CI: 1.02-2.40, p ​= ​0.042), while there were no significant associations with PM₁₀ (≥15 ​μg/m³) or ozone (≥51 ​ppb). Increased PM_2.5, PM₁₀ and ozone were independently associated with MACE (aHR ​= ​1.56, 95%CI: 1.12-2.18, p ​= ​0.008; aHR ​= ​2.09, 95%CI: 1.18-3.70, p ​= ​0.011, aHR ​= ​1.96, 95%CI: 1.20-3.21, p ​= ​0.008). In the mediation analysis, obstructive CAD accounted for 9 ​% of the total effect (p ​= ​0.012) between PM_2.5 and MACE.</p><p><strong>Conclusion: </strong>Exposure to air pollution, particularly PM_2.5, was independently associated with obstructive CAD and MACE, with obstructive CAD mediating a small but significant portion of the association between air pollution and MACE.</p>","PeriodicalId":94071,"journal":{"name":"Journal of cardiovascular computed tomography","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143665695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CT scan criteria for definition of intramural course in anomalous coronary artery with an interarterial course: A word of caution.","authors":"Giuliano Giusti, Mariantonia Villano, Andrea Busti, Antonio Dello Russo","doi":"10.1016/j.jcct.2025.03.002","DOIUrl":"https://doi.org/10.1016/j.jcct.2025.03.002","url":null,"abstract":"","PeriodicalId":94071,"journal":{"name":"Journal of cardiovascular computed tomography","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143639844","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimization and scaling of coronary CT angiography workflows in a quaternary health system.","authors":"Sadia Sultana, Mangun Randhawa, Dhrubajyoti Bandyopadhyay, Vinit Baliyan, Borek Foldyna, Nandini M Meyersohn, Albree Tower-Rader, Michael Lu, Anushri Parakh, Sandeep Hedgire, Brian B Ghoshhajra","doi":"10.1016/j.jcct.2025.02.007","DOIUrl":"https://doi.org/10.1016/j.jcct.2025.02.007","url":null,"abstract":"","PeriodicalId":94071,"journal":{"name":"Journal of cardiovascular computed tomography","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143631148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Membranous septum area and the risk of conduction abnormalities following transcatheter aortic valve implantation.","authors":"Christopher Pavitt, Timothy Bagnall, James Smethurst, George Mcinerney-Baker, Sandeep Arunothayaraj, Christopher Broyd, Michael Michail, James Cockburn, David Hildick-Smith","doi":"10.1016/j.jcct.2025.03.003","DOIUrl":"https://doi.org/10.1016/j.jcct.2025.03.003","url":null,"abstract":"<p><strong>Background: </strong>Conduction abnormalities (CA) after TAVI remain problematic. Membranous septum (MS) depth correlates inversely with new CA though within-patient variability exists.</p><p><strong>Objectives: </strong>To determine the association of CT-derived MS area with new CA after TAVI.</p><p><strong>Methods: </strong>MS depth was measured along its width (20 ​% intervals) to calculate MS area in 140 patients without CA. The primary outcome was PPI or new persistent LBBB at discharge.</p><p><strong>Results: </strong>New CA occurred in 49 (35 ​%) patients of whom 10 (7.1 ​%) required PPI and 39 (27.9 ​%) developed persisting LBBB. MS area was significantly smaller in those with new CA (20.1 [8.6] vs. 41.2 [18.0] mm2; p ​< ​0.01). By multivariable regression, a model including MS area and TAVI contact (MS width∗implant depth): MS area ratio showed better discrimination for new CA compared with a model including MS depth and MS depth - implant depth (AUC 0.89 [95 ​% CI 0.83-0.94] vs. 0.84 [95 ​% CI 0.76-0.90]; p ​= ​0.05, respectively). Optimal cut off point for correct classification of new CA for MS depth was 3.9 ​mm (sensitivity 73 ​%, specificity 76 ​%, PPV 58 ​% and NPV 84 ​%), 28.0 ​mm² for MS area (sensitivity 88 ​%, specificity 78 ​%, PPV 68 ​% and NPV 92 ​%) and 1.88 (sensitivity 63 ​%, specificity 81, PPV 77 ​% and NPV 68 ​%) for TAVI contact: MS area ratio. To minimize new CA, maximal valve implant depth should ≤ (1.88 ∗ MS area)/MS width.</p><p><strong>Conclusions: </strong>Pre-procedural assessment of the MS area offers additional predictive value for development of new conduction abnormalities after TAVI when compared with MS depth and can guide implant depth.</p>","PeriodicalId":94071,"journal":{"name":"Journal of cardiovascular computed tomography","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143588701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}