Circulation: Cardiovascular Imaging最新文献

{"title":"How to Use Multimodality Imaging for Pericarditis.","authors":"Joseph El Roumi, Jibran Ikram, Tom Kai Ming Wang, Allan L Klein","doi":"10.1161/CIRCIMAGING.125.018544","DOIUrl":"10.1161/CIRCIMAGING.125.018544","url":null,"abstract":"<p><p>Pericarditis spans acute, recurrent/incessant, effusive, and constrictive phenotypes, and accurate assessment of inflammatory activity and chronicity is essential to guide therapy and anticipate outcomes. Although transthoracic echocardiography remains the first-line modality to evaluate pericardial effusion, tamponade physiology, and constrictive hemodynamics, it is limited for tissue characterization. Multimodality imaging integrates complementary strengths: cardiac magnetic resonance provides the most sensitive noninvasive assessment of pericardial edema and late gadolinium enhancement to phenotype active inflammation versus chronic fibrotic disease and to support prognostication (including identification of potentially reversible constriction); cardiac computed tomography offers superior anatomic detail for pericardial thickness, calcification, complex effusions, and preoperative planning for pericardiectomy, and can serve as an alternative when cardiac magnetic resonance is contraindicated; and ¹⁸F-fluorodeoxyglucose positron emission tomography/computed tomography adds targeted value by detecting metabolically active pericardial inflammation in diagnostically ambiguous or refractory cases and may inform escalation to advanced therapies. We synthesize practical, guideline-aligned applications of these modalities, highlight common pitfalls and system-level constraints, and propose a simplified framework using key imaging biomarkers edema/inflammation, neovascularization (late gadolinium enhancement), thickening, effusion/tamponade, constriction, and fibrosis/calcification to enable imaging-guided therapy, including treatment escalation and tapering strategies in recurrent disease and selection of patients for pericardiectomy.</p>","PeriodicalId":10202,"journal":{"name":"Circulation: Cardiovascular Imaging","volume":" ","pages":"e018544"},"PeriodicalIF":9.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147472771","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Long-Term Stability of a Coronary-Encasing Cardiac Paraganglioma: A 4-Year Multimodality Imaging Follow-Up.","authors":"Huaijun Wang, Mary P Watkins, Anne H Atteberry, Joshua D Mitchell, Mustafa Husani, Jasvindar Singh, Gregory M Lanza","doi":"10.1161/CIRCIMAGING.125.018923","DOIUrl":"10.1161/CIRCIMAGING.125.018923","url":null,"abstract":"","PeriodicalId":10202,"journal":{"name":"Circulation: Cardiovascular Imaging","volume":" ","pages":"e018923"},"PeriodicalIF":7.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147302894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hemodynamic and Metabolic-Inflammatory Phenotyping Across the Cardiac Index Spectrum in Moderate and Severe Tricuspid Regurgitation: Prognostic Implications.","authors":"Matteo Mazzola, Nicolò De Biase, Cristina Giannini, Alessandro Sticchi, Lavinia Del Punta, Luna Gargani, Alessandro Mengozzi, Agostino Virdis, Silvia Armenia, Federica Cappelli, Emiliano Duranti, Stefano Taddei, Rebecca Hahn, David Messika-Zeitoun, Stefano Masi, Marco De Carlo, Nicola Riccardo Pugliese","doi":"10.1161/CIRCIMAGING.125.019444","DOIUrl":"10.1161/CIRCIMAGING.125.019444","url":null,"abstract":"<p><strong>Background: </strong>Moderate and severe tricuspid regurgitation (TR) is associated with poor outcomes, yet current grading systems do not fully capture circulatory heterogeneity. We investigate the relationship of cardiac index (CI) with rest-exercise hemodynamics, metabolic and inflammatory profiles, and clinical outcomes in moderate and severe TR.</p><p><strong>Methods: </strong>We prospectively enrolled 300 outpatients with atrial secondary, nonatrial secondary, and lead-associated moderate and severe TRs without ≥moderate left-sided valve disease. All underwent comprehensive laboratory profiling and ultrasound evaluation at rest and during cardiopulmonary exercise. Patients were stratified by CI tertiles and followed clinically (primary end point: all-cause mortality or heart failure hospitalization).</p><p><strong>Results: </strong>CI decreased with TR severity but showed wide interindividual variability. In patients with low CI, severe forward flow limitation was associated with more advanced right ventricle-pulmonary arterial uncoupling and biatrial dysfunction (<i>P</i><0.05 versus the other tertiles), identifying a hypodynamic-uncoupled profile. Conversely, high CI identified a hyperdynamic-congestive phenotype characterized by advanced congestion, reduced systemic vascular resistance, and heightened systemic inflammation, metabolic-nutritional derangements, and mitochondrial dysfunction (<i>P</i><0.05 versus the other tertiles). Intermediate CI showed the most favorable hemodynamic and laboratory profile. A U-shaped relationship between CI and adverse outcomes was observed, with the lowest risk at intermediate values. This pattern persisted across TR severity, cause, staging systems, and adiposity categories (<i>P</i><0.05 for all).</p><p><strong>Conclusions: </strong>In moderate and severe TRs, CI profiling captures cardiac and extracardiac determinants of flow and independently predicts outcomes beyond conventional TR grading and staging. Both low and high CIs identify high-risk patients, while an intermediate CI indicates a balanced, prognostically favorable state. CI profiling may refine risk stratification, guide individualized treatment strategies, and optimize patient selection and timing for tricuspid valve interventions.</p>","PeriodicalId":10202,"journal":{"name":"Circulation: Cardiovascular Imaging","volume":" ","pages":"e019444"},"PeriodicalIF":9.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147364316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Letter by Ji and Benharrats Regarding Article \"Artificial Intelligence-Enabled Echocardiography as a Surrogate for Multimodality Aortic Stenosis Imaging: Post Hoc Analysis of a Clinical Trial\".","authors":"David Yang Ji, Mohammed Adam Benharrats","doi":"10.1161/CIRCIMAGING.126.019718","DOIUrl":"10.1161/CIRCIMAGING.126.019718","url":null,"abstract":"","PeriodicalId":10202,"journal":{"name":"Circulation: Cardiovascular Imaging","volume":" ","pages":"e019718"},"PeriodicalIF":7.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147467212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantitative Coronary Atherosclerotic Plaque Burden From CCTA and the Benefit From Lipid-Lowering Medication.","authors":"Teemu Maaniitty, Sarah Bär, Jeroen J Bax, Juhani Knuuti, Antti Saraste","doi":"10.1161/CIRCIMAGING.125.018840","DOIUrl":"10.1161/CIRCIMAGING.125.018840","url":null,"abstract":"<p><strong>Background: </strong>We hypothesized that quantification of coronary atherosclerotic plaque burden by artificial intelligence-guided quantitative computed tomography can identify patients who derive outcome benefit from lipid-lowering medication (LLM).</p><p><strong>Methods: </strong>In this observational cohort study, consecutive symptomatic patients undergoing coronary computed tomography angiography for suspected coronary artery disease (CAD) were assessed for percent atheroma volume (PAV) by artificial intelligence-guided quantitative computed tomography. The use of LLM was assessed based on drug purchase registry data within 6 months after coronary computed tomography angiography. Patients were followed for the composite of death, myocardial infarction, and unstable angina for a median of 6.9 years.</p><p><strong>Results: </strong>Among 2269 patients (median age, 63 years; 42% men), 1261 (56%) patients used LLM after coronary computed tomography angiography, and 255 (11%) experienced the composite end point during follow-up. The median PAV was 6.6% among users and 1.4% among nonusers of LLM (<i>P</i><0.001). Adapting the previously proposed CAD stages for artificial intelligence-guided quantitative computed tomography, the use of LLM (versus no use) was associated with improved outcomes among the 910 patients with PAV >5% (annual event rate, 2.62% versus 4.14%; adjusted <i>P</i>=0.002), even in the absence of obstructive CAD, but not among the 1359 patients with PAV ≤5% (annual event rate, 0.94% versus 0.65%; adjusted <i>P</i>=0.717). An adjusted Cox regression analysis, including interaction between PAV and LLM, suggested a PAV threshold between 4% and 10% for gaining prognostic benefit from LLM.</p><p><strong>Conclusions: </strong>In symptomatic patients with suspected CAD, LLM after coronary computed tomography angiography was associated with a lower rate of adverse events during long-term follow-up among those with PAV >5%, even in the absence of obstructive CAD. The quantification of coronary atherosclerotic plaque burden is a potential marker to guide preventive lipid-lowering therapy.</p>","PeriodicalId":10202,"journal":{"name":"Circulation: Cardiovascular Imaging","volume":" ","pages":"e018840"},"PeriodicalIF":9.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13095065/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147622002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Decoding the TR Enigma: Cardiac Index Phenotypes, Congestion, and Prognosis.","authors":"Steele C Butcher, David Playford","doi":"10.1161/CIRCIMAGING.126.019694","DOIUrl":"10.1161/CIRCIMAGING.126.019694","url":null,"abstract":"","PeriodicalId":10202,"journal":{"name":"Circulation: Cardiovascular Imaging","volume":" ","pages":"e019694"},"PeriodicalIF":7.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147621997","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Combined Anatomic and Functional Testing Identifies Patients With Obstructive Coronary Artery Disease Who Benefit From Revascularization.","authors":"Oliver Buchhave Pedersen, Laust Dupont Rasmussen, Jacob Hartmann Søby, Lars C Gormsen, Evald Høj Christiansen, Juhani Knuuti, Morten Bøttcher, Leslee Shaw, Simon Winther","doi":"10.1161/CIRCIMAGING.125.019267","DOIUrl":"10.1161/CIRCIMAGING.125.019267","url":null,"abstract":"<p><strong>Background: </strong>In patients with obstructive coronary artery disease, early revascularization does not improve outcomes but may reduce angina symptoms. The objective of this study was to examine whether changes in health status outcomes following revascularization are explained by the extent of myocardial perfusion defects and improvement in myocardial perfusion.</p><p><strong>Methods: </strong>Two trials enrolling stable patients with new-onset chest pain suggestive of obstructive coronary artery disease, the ISCHEMIA (International Study of Comparative Health Effectiveness With Medical and Invasive Approaches) and the Dan-NICAD (Danish Study of Noninvasive Testing in Coronary Artery Disease) trials, were analyzed. Patients with single-vessel coronary artery disease who underwent nuclear myocardial perfusion imaging (nMPI) were included. In the ISCHEMIA trial, patients with moderate/severe ischemia were randomized to either optimal medical therapy alone or optimal medical therapy and invasive angiography. The Dan-NICAD trial enrolled patients with suspected stenosis on coronary computed tomographic angiography undergoing nMPI. Test-guided revascularization blinded to nMPI was performed, and patients with initially abnormal nMPI were reassessed after 12 months. The primary outcome was the change in the Seattle Angina Questionnaire angina frequency score.</p><p><strong>Results: </strong>In total, 584 patients were eligible. In patients with a summed difference score of 5≤10 (n=149 [25%]) and ≥10 (n=152 [26%]), revascularization was associated with an improved angina frequency score (mean change ±SD: 16.4±20.9 and 19.0±24.1). No improvement was demonstrated in patients with a summed difference score <5. In multivariable logistic regression analysis (n=91), an increase in hyperemic myocardial blood flow at follow-up was associated with freedom from angina (odds ratio, 2.89 [95% CI, 1.04-8.70]).</p><p><strong>Conclusions: </strong>In patients with single-vessel coronary artery disease, nMPI may identify patients more likely to experience improved symptoms from revascularization, potentially reflecting enhanced myocardial perfusion.</p>","PeriodicalId":10202,"journal":{"name":"Circulation: Cardiovascular Imaging","volume":" ","pages":"e019267"},"PeriodicalIF":9.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13007719/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147484891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diagnosis of Cardiac Amyloidosis on Echocardiography Using Artificial Intelligence.","authors":"Adam Ioannou, Michel G Khouri, Takeshi Kitai, Sreekanth Vemulapalli, Chung-Lieh Hung, Sze Chi Lim, Matthew Frost, Weile Wayne Tee, Josephine Mansell, Awais Sheikh, Lucia Venneri, Yousuf Razvi, Aldostefano Porcari, Ana Martinez-Naharro, Muhammad U Rauf, Helen Lachmann, Philip N Hawkins, Ashutosh Wechelakar, William Moody, Francesco Bandera, Justin A Ezekowitz, Carolyn S P Lam, Scott D Solomon, Julian D Gillmore, Marianna Fontana","doi":"10.1161/CIRCIMAGING.125.018991","DOIUrl":"10.1161/CIRCIMAGING.125.018991","url":null,"abstract":"<p><strong>Background: </strong>Diagnosing cardiac amyloidosis (CA) on echocardiography can be challenging due to the imaging overlap between CA and more prevalent causes of a hypertrophic phenotype. This study sought to (1) evaluate the performance of artificial-intelligence (AI) derived measurements incorporated into the established multiparametric echocardiographic scoring system to detect CA; (2) develop and validate an AI-based deep-learning model for video-based detection of CA on echocardiography.</p><p><strong>Methods: </strong>The study population comprised 5776 patients (CA, 2756; controls, 3020). The training data set included patients from the UK National Amyloidosis Center and Taiwan MacKay Memorial Hospital (CA, 2241; controls, 2130). External test data sets were obtained from the US Duke University Health System (CA, 334; left ventricular hypertrophy controls, 668) and Japan National Cerebral and Cardiovascular Center (CA, 181; left ventricular hypertrophy controls, 222).</p><p><strong>Results: </strong>The multiparametric echocardiographic score computed using AI-derived measurements achieved an accuracy of 79.5% (sensitivity, 75.4%; specificity, 81.5%) in the United States cohort and 79.7% (sensitivity, 81.6%; specificity, 78.1%) in the Japan cohort. The deep-learning model demonstrated accuracies of 96.2% (sensitivity, 96.8%; specificity, 95.7%) and 95.8% (sensitivity, 97.3%; specificity, 94.3%) in the internal validation and internal test sets, respectively. External validation of the deep-learning model showed accuracies of 87.5% (sensitivity, 86.6%; specificity, 87.9%) in the United States and 88.4% (sensitivity, 92.3%; specificity, 85.3%) in the Japanese cohort. Subgroup analysis demonstrated that the deep-learning model showed robust discrimination of CA from other hypertrophic phenocopies: CA versus hypertension (area under the curve [AUC], 0.92 [95% CI, 0.91-0.94]), CA versus hypertrophic cardiomyopathy (AUC, 0.91 [95% CI, 0.87-0.94]), CA versus aortic stenosis (AUC, 0.93 [95% CI, 0.90-0.95]), CA versus chronic kidney disease (AUC, 0.93 [95% CI, 0.91-0.95]). The deep-learning model was able to classify a greater proportion of patients compared with the AI-derived multiparametric echocardiographic score and achieved superior diagnostic accuracy (AUC, 0.93 [95% CI, 0.91-0.95] versus AUC, 0.88 [95% CI, 0.85-0.90]; <i>P</i><0.001).</p><p><strong>Conclusions: </strong>Both the multiparametric echocardiographic score computed from AI-derived measurements and the fully automated deep-learning model can accurately identify patients with CA in globally diverse cohorts, with the deep-learning model providing superior performance.</p>","PeriodicalId":10202,"journal":{"name":"Circulation: Cardiovascular Imaging","volume":" ","pages":"e018991"},"PeriodicalIF":9.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13095060/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146164492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Response by Oikonomou et al to Letter Regarding Article, \"Artificial Intelligence-Enabled Echocardiography as a Surrogate for Multimodality Aortic Stenosis Imaging: Post Hoc Analysis of a Clinical Trial\".","authors":"Evangelos K Oikonomou, Marc R Dweck, Rohan Khera","doi":"10.1161/CIRCIMAGING.126.019756","DOIUrl":"10.1161/CIRCIMAGING.126.019756","url":null,"abstract":"","PeriodicalId":10202,"journal":{"name":"Circulation: Cardiovascular Imaging","volume":" ","pages":"e019756"},"PeriodicalIF":9.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13088801/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147572175","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PET-Derived Renal Perfusion Provides a Window Into Cardiorenal Risk Beyond Filtration.","authors":"Krishna K Patel, Vasken Dilsizian","doi":"10.1161/CIRCIMAGING.126.019570","DOIUrl":"10.1161/CIRCIMAGING.126.019570","url":null,"abstract":"","PeriodicalId":10202,"journal":{"name":"Circulation: Cardiovascular Imaging","volume":" ","pages":"e019570"},"PeriodicalIF":9.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12912788/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146149289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}