Radiology. Cardiothoracic imaging最新文献

{"title":"Role of Artificial Intelligence in Detecting and Classifying Aortic Dissection: Where Are We? A Systematic Review and Meta-Analysis.","authors":"Ashar Asif, Maha Alsayyari, Dorothy Monekosso, Paolo Remagnino, Raghuram Lakshminarayan","doi":"10.1148/ryct.240353","DOIUrl":"10.1148/ryct.240353","url":null,"abstract":"<p><p>Purpose To evaluate the diagnostic performance of artificial intelligence (AI) models in detecting and classifying aortic dissection (AD) from CT images through a systematic review and meta-analysis. Materials and Methods PubMed, Web of Science, Embase, and Medline were searched for articles published from January 2010 to October 2023. All primary studies were included. Quality of evidence was assessed using a composite tool based on the METhodological RadiomICs Score (ie, METRICS) and Checklist for Artificial Intelligence in Medical Imaging (ie, CLAIM) checklists, and risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 (ie, QUADAS-2) tool. Univariate and bivariate meta-analyses were performed assessing individual and joint estimates of sensitivity and specificity. Results Thirteen studies were identified, with most using contrast-enhanced CT (CECT) imaging (<i>n</i> = 9) and the remainder using noncontrast CT (NCCT) imaging as their model input. Only three studies presented algorithms classifying AD by Stanford criteria. Univariate analysis of AI detection performance estimated sensitivity at 94% (95% CI: 88, 97; <i>P</i> = .049) and specificity at 88% (95% CI: 79, 94; <i>P</i> < .001). Bivariate analysis showed good overall model performances (area under the receiver operating characteristic curve [AUC], 0.97 [95% CI: 0.95, 0.99]; <i>P</i> = .49). Subgroup analyses revealed good performance for models using CECT images (sensitivity, 97% [95% CI: 81, 100; <i>P</i> = .007]; specificity, 93% [95% CI: 87, 97; <i>P</i> < .001]; AUC, 0.98 [95% CI: 0.93, 0.99; <i>P</i> = .09]) and NCCT images (sensitivity, 91% [95% CI: 83, 96; <i>P</i> = .33); specificity, 84% [95% CI: 69, 93; <i>P</i> < .001); AUC, 0.95 [95% CI: 0.90, 0.99; <i>P</i> = .14]). Most studies were of low quality and had high risk of bias. Conclusion AI can feasibly detect AD but does not demonstrate clinical applicability in its current form. <b>Keywords:</b> CT, Vascular, Cardiac, Aorta, Computer-aided Diagnosis (CAD), Meta-Analysis <i>Supplemental material is available for this article.</i> © RSNA, 2025.</p>","PeriodicalId":21168,"journal":{"name":"Radiology. Cardiothoracic imaging","volume":"7 3","pages":"e240353"},"PeriodicalIF":3.8,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144275758","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":"The Distribution of Coronary Plaque Volumes across CAD-RADS Categories: A PRECISE Substudy.","authors":"Ruurt A Jukema, Philipp Blanke, John K Khoo, Aaisha Ferkh, Maya Miller, Pamela S Douglas, Jonathon A Leipsic","doi":"10.1148/ryct.240461","DOIUrl":"https://doi.org/10.1148/ryct.240461","url":null,"abstract":"","PeriodicalId":21168,"journal":{"name":"Radiology. Cardiothoracic imaging","volume":"7 3","pages":"e240461"},"PeriodicalIF":3.8,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144209392","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":"Pericoronary Adipose Tissue Attenuation in Patients with Future Acute Coronary Syndromes: The ICONIC Study.","authors":"Alan C Kwan, Evangelos Tzolos, Eyal Klein, Donghee Han, Andrew Lin, Keiichiro Kuronuma, Billy Chen, Guadalupe Flores Tomasino, Heidi Gransar, Piotr J Slomka, Susan Cheng, Catherine Gebhard, Philipp Kaufmann, Jeroen J Bax, Filippo Cademartiri, Kavitha Chinnaiyan, Benjamin J W Chow, Edoardo Conte, Ricardo C Cury, Gudrun Feuchtner, Martin Hadamitzky, Yong-Jin Kim, Jonathon A Leipsic, Erica Maffei, Hugo Marques, Fabian Plank, Gianluca Pontone, Todd C Villines, Mouaz H Al-Mallah, Pedro de Araújo Gonçalves, Ibrahim Danad, Yao Lu, Ji-Hyun Lee, Sang-Eun Lee, Lohendran Baskaran, Subhi J Al'Aref, Matthew J Budoff, Habib Samady, Peter H Stone, Renu Virmani, Stephan Achenbach, Jagat Narula, Hyuk-Jae Chang, Leslee J Shaw, Daniel S Berman, Fay Lin, Damini Dey","doi":"10.1148/ryct.240200","DOIUrl":"10.1148/ryct.240200","url":null,"abstract":"<p><p>Purpose Pericoronary adipose tissue attenuation (PCATa) measured at coronary CT angiography (CCTA) is an imaging biomarker of coronary inflammation associated with long-term adverse cardiac events. The authors hypothesized that PCATa may independently identify patients at risk for acute coronary syndromes (ACS). Materials and Methods The authors performed a retrospective substudy of the Incident Coronary Syndromes Identified by Computed Tomography (ICONIC) study, a propensity-matched case-control study of patients with CCTA followed by ACS. Two hundred analyzable case and control pairs were identified from the original 234 pairs. PCATa was measured using the adjusted attenuation of fat around proximal coronary vessels. The primary analysis applied conditional Cox models with cluster-robust standard errors to predict patient-level incident ACS, with adjustment for quantitative plaque volumes and clinical reporting-oriented findings of maximal stenosis and high-risk plaque features (HRPF). Results A total of 400 patients with 1174 matched measurable vessels were included. PCATa was not significantly different between patients with future ACS versus controls (-72.99 HU ± 9.42 vs -73.96 HU ± 9.47; <i>P</i> = .08). Conversely, PCATa was significantly associated with incident ACS events in Cox models (adjusted for noncalcified plaque hazard ratio [HR]: 1.015; 95% CI: 1.001, 1.028; <i>P</i> = .03; adjusted for total plaque HR: 1.015; 95% CI: 1.002, 1.029; <i>P</i> = .03; adjusted for stenosis and HRPF HR: 1.014; 95% CI: 1.000, 1.028; <i>P</i> = .049). Conclusion Limited quantitative difference in PCATa between patients and controls matched for risk factors and coronary artery disease suggests that PCATa may not be a useful single marker to identify future ACS. Nonetheless, significant differences seen in adjusted survival models identify a small biologic effect for increased risk of future ACS independent of traditional risk factors. <b>Keywords:</b> CT-Angiography, Inflammation, Coronary Arteries, Acute Coronary Syndrome, Pericoronary Adipose Tissue Attenuation, Noncalcified Plaque, ICONIC Study, Cardiovascular Risk Clinical trials registration no. NCT02959099 <i>Supplemental material is available for this article.</i> © RSNA, 2025.</p>","PeriodicalId":21168,"journal":{"name":"Radiology. Cardiothoracic imaging","volume":"7 3","pages":"e240200"},"PeriodicalIF":3.8,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144333753","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":"Establishing Cardiac MRI Reference Ranges Stratified by Sex and Age for Cardiovascular Function during Exercise.","authors":"Ronny Schweitzer, Antonio de Marvao, Mit Shah, Paolo Inglese, Peter Kellman, Alaine Berry, Ben Statton, Declan P O'Regan","doi":"10.1148/ryct.240175","DOIUrl":"https://doi.org/10.1148/ryct.240175","url":null,"abstract":"<p><p>Purpose To evaluate the effects of exercise on left ventricular parameters using exercise cardiac MRI in healthy adults without known cardiovascular disease and establish reference ranges stratified by age and sex. Materials and Methods This prospective study included healthy adult participants with no known cardiovascular disease or genetic variants associated with cardiomyopathy, enrolled between January 2018 and April 2021, who underwent exercise cardiac MRI evaluation. Participants were imaged at rest and after exercise, and parameters were measured by two readers. Prediction intervals were calculated and compared across sex and age groups. Results The study included 161 participants (mean age, 49 years ± 14 [SD]; 85 female). Compared with the resting state, exercise caused an increase in heart rate (64 beats per minute ± 9 vs 133 beats per minute ± 19, <i>P</i> < .001), left ventricular end-diastolic volume (140 mL ± 32 vs 148 mL ± 35, <i>P</i> < .001), stroke volume (82 mL ± 18 vs 102 mL ± 25, <i>P</i> < .001), ejection fraction (59% ± 6 vs 69% ± 7, <i>P</i> < .001), and cardiac output (5.2 L/min ± 1.1 vs 13.5 L/min ± 3.9, <i>P</i> < .001) and a decrease in left ventricular end-systolic volume (58 mL ± 18 vs 46 mL ± 15, <i>P</i> < .001). There were statistically significant differences in exercise response between groups stratified by sex and age for most parameters. Conclusion In healthy adults, an increase in cardiac output after exercise was driven by an increase in heart rate with both increased ventricular filling and emptying. Normal ranges for exercise response, stratified by age and sex, were established as a reference for the use of exercise cardiac MRI in clinical practice. <b>Keywords:</b> Cardiac, MR Imaging, Heart, Physiological Studies <i>Supplemental material is available for this article.</i> © RSNA, 2025.</p>","PeriodicalId":21168,"journal":{"name":"Radiology. Cardiothoracic imaging","volume":"7 3","pages":"e240175"},"PeriodicalIF":3.8,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144226400","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":"Performance of a Chest Radiograph-based Deep Learning Model for Detecting Hepatic Steatosis.","authors":"Daiju Ueda, Sawako Uchida-Kobayashi, Akira Yamamoto, Shannon L Walston, Hiroyuki Motoyama, Hideki Fujii, Toshio Watanabe, Yukio Miki, Norifumi Kawada","doi":"10.1148/ryct.240402","DOIUrl":"10.1148/ryct.240402","url":null,"abstract":"<p><p>Purpose To develop and evaluate a deep learning model for detecting hepatic steatosis using chest radiographs. Materials and Methods This retrospective study included consecutively collected chest radiographs from patients who underwent controlled attenuation parameter (CAP) examinations at two institutions from November 2013 to May 2023. All patients were diagnosed as having or not having hepatic steatosis based on CAP value. Patients from one institution were randomly divided into training, tuning, and internal test sets using an 8:1:1 ratio. Patients from the other institution comprised an external test set. A deep learning-based model to classify hepatic steatosis using chest radiographs was trained, tuned, and evaluated. Model performance on the internal and external test sets was assessed using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. Results In total, 6599 radiographs associated with 6599 CAP examinations obtained in 4414 patients were included. The internal test set included 529 radiographs from 363 patients (mean age, 56 years ± 11 [SD]; 344 male patients). The external test set included 1100 radiographs from 783 patients (mean age, 58 years ± 16; 604 male patients). The AUC, accuracy, sensitivity, and specificity (with 95% CIs) for the internal test set were 0.83 (0.79, 0.86), 77% (74, 81), 68% (61, 75), and 82% (77, 85), respectively. For the external test set, the values were 0.82 (0.79, 0.85), 76% (73, 78), 76% (69, 81), and 76% (73, 79), respectively. Conclusion The developed deep learning model showed good performance for detecting hepatic steatosis using chest radiographs. <b>Keywords:</b> Liver, Hepatic Steatosis, Chest Radiography, Controlled Attenuation Parameter <i>Supplemental material is available for this article.</i> © RSNA, 2025.</p>","PeriodicalId":21168,"journal":{"name":"Radiology. Cardiothoracic imaging","volume":"7 3","pages":"e240402"},"PeriodicalIF":3.8,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144333752","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":"Feasibility of Gadolinium-enhanced T1* Ratio Mapping for Myocardial Tissue Characterization.","authors":"Daniel Schroth, Giulia Essert, Jochen Hansmann, Markus Haass, Marco Ochs","doi":"10.1148/ryct.240060","DOIUrl":"https://doi.org/10.1148/ryct.240060","url":null,"abstract":"<p><p>Purpose To evaluate the diagnostic performance of T1* ratio mapping, a novel postprocessing algorithm applied to standard inversion time (TI) scout images for cardiac tissue characterization. Materials and Methods This retrospective study included patients who underwent cardiac MRI examinations between 2015 and 2023 and were diagnosed with cardiac amyloidosis (CA), myocarditis, dilated cardiomyopathy (DCM), hypertrophic cardiomyopathy (HCM), or no disease (healthy controls). Motion-corrected postcontrast T1* ratio maps were generated from TI scout images using blood pool, skeletal muscle, and spleen as reference tissues. Diagnostic performance was evaluated using receiver operating characteristic curve analysis; group differences were assessed with nonparametric tests; and correlations between T1* ratio mapping and late gadolinium enhancement (LGE) quantification were analyzed. Results The study included 130 patients (mean age, 63 years ± 21 [SD]; 90 male patients; 30 with CA, 20 with myocarditis, 20 with DCM, 30 with HCM, and 30 controls). Spleen-referenced T1* ratio showed the highest area under the receiver operating characteristic curve (AUC) of the reference tissues for distinguishing pooled disease cases from controls (AUC = 0.76 [95% CI: 0.68, 0.84]). It achieved excellent discriminatory ability for CA cases versus controls (AUC > 0.99 [95% CI: >0.99, >0.99]), CA versus other pooled diseases (AUC = 0.97 [95% CI: 0.94, >0.99]), and differentiating affected from unaffected myocarditis segments (AUC = 0.93 [95% CI: 0.86, 0.98]). Spleen-referenced T1* ratio strongly correlated with LGE quantification (<i>R</i> = 0.85) and identified a greater extent of myocardial involvement than LGE in cardiomyopathies (<i>r</i>_b = 0.22). Conclusion T1* ratio mapping showed potential in identifying pathologic myocardial changes in various conditions. Easy integration into existing setups as a postprocessing algorithm may facilitate broader access to myocardial tissue characterization. <b>Keywords:</b> MRI, Cardiomyopathies, Tissue Characterization <i>Supplemental material is available for this article.</i> © RSNA, 2025.</p>","PeriodicalId":21168,"journal":{"name":"Radiology. Cardiothoracic imaging","volume":"7 3","pages":"e240060"},"PeriodicalIF":3.8,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144275757","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":"Charting the Future of Myocarditis Prognostication: Embracing Longitudinal Imaging, Advanced Metrics, and External Validation.","authors":"Furkan Ufuk","doi":"10.1148/ryct.250085","DOIUrl":"https://doi.org/10.1148/ryct.250085","url":null,"abstract":"","PeriodicalId":21168,"journal":{"name":"Radiology. Cardiothoracic imaging","volume":"7 3","pages":"e250085"},"PeriodicalIF":3.8,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144275756","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":"Functional Lung Imaging Using CT: An Update.","authors":"Changhyun Lee, Juergen Biederer, Yoshiharu Ohno, Joon Beom Seo, Grace Parraga, David L Levin, James C Gee, Rohit Jena, Yoshiyuki Ozawa, Mark O Wielpuetz, Eric A Hoffman, Edwin J R van Beek","doi":"10.1148/ryct.240505","DOIUrl":"https://doi.org/10.1148/ryct.240505","url":null,"abstract":"<p><p>Chest CT has become a key component of the diagnostic approach to a wide range of airway and vascular diseases, including asthma, emphysema, chronic airways disease, and pulmonary vascular disorders such as pulmonary embolism. The interaction between ventilation and perfusion is complex but is always aimed at optimal matching to enable efficient gas exchange. If either one or both of these are affected by disease, they have a negative effect on the other. CT is able to define the structure of lung parenchyma, airways, and pulmonary vasculature in great detail. Beyond morphology, increasingly sophisticated scanner and software technology increase the diagnostic scope of CT toward obtaining comprehensive functional information. This paves the way for new understanding of lung function, the effects of various diseases, and the way in which therapeutic interventions have an effect. Greater understanding of the principal components of chest CT and how they are developing into clinical practice is relevant to anyone with an interest in diagnostic chest imaging. <b>Keywords:</b> CT-Spectral Imaging (Dual Energy), Applications-CT, CT-Quantitative, CT-Perfusion, Thorax, Lung © RSNA, 2025.</p>","PeriodicalId":21168,"journal":{"name":"Radiology. Cardiothoracic imaging","volume":"7 3","pages":"e240505"},"PeriodicalIF":3.8,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144226401","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":"<i>Radiology: Cardiothoracic Imaging</i> Highlights 2024.","authors":"Roberta Catania, Aprateem Mukherjee, Jordan H Chamberlin, Francisco Calle, Preethi Philomina, Domenico Mastrodicasa, Bradley D Allen, Dominika Suchá, Suhny Abbara, Kate Hanneman","doi":"10.1148/ryct.250064","DOIUrl":"https://doi.org/10.1148/ryct.250064","url":null,"abstract":"<p><p><i>Radiology: Cardiothoracic Imaging</i> publishes research, technical developments, and reviews related to cardiac, vascular, and thoracic imaging. The current review article, led by the <i>Radiology: Cardiothoracic Imaging</i> trainee editorial board, highlights the most impactful articles published in the journal between November 2023 and October 2024. The review encompasses various aspects of cardiac, vascular, and thoracic imaging related to coronary artery disease, cardiac MRI, valvular imaging, congenital and inherited heart diseases, thoracic imaging, lung cancer, artificial intelligence, and health services research. Key highlights include the role of CT fractional flow reserve analysis to guide patient management, the role of MRI elastography in identifying age-related myocardial stiffness associated with increased risk of heart failure, review of MRI in patients with cardiovascular implantable electronic devices and fractured or abandoned leads, imaging of mitral annular disjunction, specificity of the Lung Imaging Reporting and Data System version 2022 for detecting malignant airway nodules, and a radiomics-based reinforcement learning model to analyze serial low-dose CT scans in lung cancer screening. Ongoing research and future directions include artificial intelligence tools for applications such as plaque quantification using coronary CT angiography and growing understanding of the interconnectedness of environmental sustainability and cardiovascular imaging. <b>Keywords:</b> CT, MRI, CT-Coronary Angiography, Cardiac, Pulmonary, Coronary Arteries, Heart, Lung, Mediastinum, Mitral Valve, Aortic Valve, Artificial Intelligence © RSNA, 2025.</p>","PeriodicalId":21168,"journal":{"name":"Radiology. Cardiothoracic imaging","volume":"7 3","pages":"e250064"},"PeriodicalIF":3.8,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144226459","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":"Cardiac Computed Tomography for Prosthetic Heart Valve Assessment. An Expert Consensus Document of the Society of Cardiovascular Computed Tomography (SCCT), the American College of Cardiology (ACC), the European Society of Cardiovascular Radiology (ESCR), the North American Society of Cardiovascular Imaging (NASCI), the Radiological Society of North America (RSNA), the Society for Cardiovascular Angiography & Interventions (SCAI), and Society of Thoracic Surgeons (STS)^1,2.","authors":"Ricardo P J Budde, Marguerite E Faure, Suhny Abbara, Hatem Alkadhi, Paul C Cremer, Gudrun M Feuchtner, Holly M Gonzales, Todd L Kiefer, Jonathan Leipsic, Koen Nieman, Jonathan Revels, Dee Dee Wang, Eric Williamson, Moritz C Wyler von Ballmoos, Brittany A Zwischenberger, Rodrigo Salgado","doi":"10.1148/ryct.250231","DOIUrl":"https://doi.org/10.1148/ryct.250231","url":null,"abstract":"<p><p>Prosthetic heart valve (PHV) dysfunction is increasingly seen due to the increase in the number of PHV that are being implanted worldwide. Cardiac CT imaging has emerged as a valuable tool to assess PHVs and determine the cause of dysfunction. This consensus document first summarizes the available techniques for PHV assessment. Then the use of CT in PHV (dys)function assessment is discussed in detail including consensus statements for correct indications and patient selection for CT assessment of PHVs, image acquisition, reconstruction and measurement protocols and how to interpret and report the CT findings for specific types of PHV dysfunction. This article was published in <i>Journal of Cardiovascular Computed Tomography</i>, Budde RPJ et al, Cardiac computed tomography for prosthetic heart valve assessment: An expert consensus document of the Society of Cardiovascular Computed Tomography (SCCT), the American College of Cardiology (ACC), the European Society of Cardiovascular Radiology (ESCR), the North American Society for Cardiovascular Imaging (NASCI), the Radiological Society of North America (RSNA), the Society for Cardiovascular Angiography & Interventions (SCAI), and the Society of Thoracic Surgeons (STS). The articles are identical except for minor stylistic and spelling differences in keeping with each journal's style. Either citation may be used when referencing this document. © 2025 Society of Cardiovascular Computed Tomography. Published by Elsevier Inc. on behalf of the Society of Cardiovascular Computed Tomography. This is an open access article under the CC BY-NC-ND license (<i>http://creativecommons.org/licenses/by-nc-nd/4.0/</i>). <b>Keywords:</b> Computed Tomography, Valve, Cardiac, Imaging, Dysfunction, Prosthetic.</p>","PeriodicalId":21168,"journal":{"name":"Radiology. Cardiothoracic imaging","volume":"7 3","pages":"e250231"},"PeriodicalIF":3.8,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144317819","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}