Pub Date : 2026-01-01DOI: 10.1016/j.jcmg.2025.07.022
Arthur Shiyovich MD , Avinainder Singh MBBS , Camila V. Blair MD , Rhanderson Cardoso MD , Daniel Huck MD, MPH , Gary Peng MD, PhD , Leslee J. Shaw PhD , Jonathon A. Leipsic MD , Christoph Gräni MD , Charalambos Antoniades MD, PhD , Pál Maurovich-Horvat MD, PhD, MPH , Eric E. Williamson MD , Filippo Cademartiri MD, PhD , Stephan Achenbach MD , Ron Blankstein MD
Coronary computed tomography angiography plays a pivotal role in the diagnosis, risk stratification, and treatment of patients with known or suspected coronary artery disease. However, conventional computed tomography (CT) technologies are limited by spatial resolution, artifact susceptibility, and radiation exposure. Photon-counting computed tomography (PCCT) introduces substantial technological improvements over conventional CT. This includes improved spatial and contrast resolution, energy discrimination, and reduction of various artifacts. As a result, PCCT enables superior coronary lumen and plaque evaluation, even in complex cases with severe calcification or smaller coronary stents. Beyond the coronary arteries, PCCT offers improved visualization of cardiac anatomy and myocardial tissue characterization with the potential to reduce downstream testing, improve diagnosis and treatment, and ultimately improve clinical outcomes. PCCT is poised to become the dominant technology for cardiovascular CT; however, challenges such as high costs, increased data demands, and a need for more validation, standardized image acquisition, and post-processing protocols remain. This review explores the technical principles of PCCT, its advantages over conventional CT, and its current and potential future applications in cardiac imaging, highlighting opportunities for future research.
{"title":"Photon-Counting Computed Tomography in Cardiac Imaging","authors":"Arthur Shiyovich MD , Avinainder Singh MBBS , Camila V. Blair MD , Rhanderson Cardoso MD , Daniel Huck MD, MPH , Gary Peng MD, PhD , Leslee J. Shaw PhD , Jonathon A. Leipsic MD , Christoph Gräni MD , Charalambos Antoniades MD, PhD , Pál Maurovich-Horvat MD, PhD, MPH , Eric E. Williamson MD , Filippo Cademartiri MD, PhD , Stephan Achenbach MD , Ron Blankstein MD","doi":"10.1016/j.jcmg.2025.07.022","DOIUrl":"10.1016/j.jcmg.2025.07.022","url":null,"abstract":"<div><div>Coronary computed tomography angiography plays a pivotal role in the diagnosis, risk stratification, and treatment of patients with known or suspected coronary artery disease. However, conventional computed tomography (CT) technologies are limited by spatial resolution, artifact susceptibility, and radiation exposure. Photon-counting computed tomography (PCCT) introduces substantial technological improvements over conventional CT. This includes improved spatial and contrast resolution, energy discrimination, and reduction of various artifacts. As a result, PCCT enables superior coronary lumen and plaque evaluation, even in complex cases with severe calcification or smaller coronary stents. Beyond the coronary arteries, PCCT offers improved visualization of cardiac anatomy and myocardial tissue characterization with the potential to reduce downstream testing, improve diagnosis and treatment, and ultimately improve clinical outcomes. PCCT is poised to become the dominant technology for cardiovascular CT; however, challenges such as high costs, increased data demands, and a need for more validation, standardized image acquisition, and post-processing protocols remain. This review explores the technical principles of PCCT, its advantages over conventional CT, and its current and potential future applications in cardiac imaging, highlighting opportunities for future research.</div></div>","PeriodicalId":14767,"journal":{"name":"JACC. Cardiovascular imaging","volume":"19 1","pages":"Pages 94-117"},"PeriodicalIF":15.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145246912","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}
Pub Date : 2026-01-01DOI: 10.1016/j.jcmg.2025.07.008
Niklas Beyhoff MD, Owen P. Agnel MSc, Maximilian Fenski MD, Christina Botrous MBBS, MSc, Yi Jie Gifford Tan BA, BM BCh, Robert W. Smillie BA, BM BCh, Zakariye Ashkir MD, Lucy E.M. Finnigan PhD, Sarahfaye F. Dolman MPH, Paul Kolm PhD, William S. Weintraub MD, Raymond Y. Kwong MD, MPH, Michael Jerosch-Herold PhD, Milind Y. Desai MD, Carolyn Y. Ho MD, Patrice Desvigne-Nickens MD, John P. DiMarco MD, Barbara Casadei MD, DPhil, Hugh C. Watkins MD, PhD, Christopher M. Kramer MD, Betty Raman MBBS, DPhil
{"title":"Left Atrial Reservoir Strain Predicts Atrial Fibrillation in Hypertrophic Cardiomyopathy","authors":"Niklas Beyhoff MD, Owen P. Agnel MSc, Maximilian Fenski MD, Christina Botrous MBBS, MSc, Yi Jie Gifford Tan BA, BM BCh, Robert W. Smillie BA, BM BCh, Zakariye Ashkir MD, Lucy E.M. Finnigan PhD, Sarahfaye F. Dolman MPH, Paul Kolm PhD, William S. Weintraub MD, Raymond Y. Kwong MD, MPH, Michael Jerosch-Herold PhD, Milind Y. Desai MD, Carolyn Y. Ho MD, Patrice Desvigne-Nickens MD, John P. DiMarco MD, Barbara Casadei MD, DPhil, Hugh C. Watkins MD, PhD, Christopher M. Kramer MD, Betty Raman MBBS, DPhil","doi":"10.1016/j.jcmg.2025.07.008","DOIUrl":"10.1016/j.jcmg.2025.07.008","url":null,"abstract":"","PeriodicalId":14767,"journal":{"name":"JACC. Cardiovascular imaging","volume":"19 1","pages":"Pages 133-135"},"PeriodicalIF":15.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144955006","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}
Pub Date : 2026-01-01DOI: 10.1016/j.jcmg.2025.07.020
Michael W. Lim MBBS, Rahul G. Muthalaly MBBS, MPH, Geoffrey R. Wong MBBS, PhD, Ahmed M. Al-Kaisey MBChB, PhD, Damini Dey PhD, Thomas H. Marwick MBBS, MPH, PhD, Peter M. Kistler MBBS, PhD, Jonathan M. Kalman MBBS, PhD, Nitesh Nerlekar MBBS, MPH, PhD
{"title":"Peripulmonary Vein Adipose Tissue Attenuation as a Novel Marker of Atrial Fibrillation Risk","authors":"Michael W. Lim MBBS, Rahul G. Muthalaly MBBS, MPH, Geoffrey R. Wong MBBS, PhD, Ahmed M. Al-Kaisey MBChB, PhD, Damini Dey PhD, Thomas H. Marwick MBBS, MPH, PhD, Peter M. Kistler MBBS, PhD, Jonathan M. Kalman MBBS, PhD, Nitesh Nerlekar MBBS, MPH, PhD","doi":"10.1016/j.jcmg.2025.07.020","DOIUrl":"10.1016/j.jcmg.2025.07.020","url":null,"abstract":"","PeriodicalId":14767,"journal":{"name":"JACC. Cardiovascular imaging","volume":"19 1","pages":"Pages 130-132"},"PeriodicalIF":15.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145017868","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}
Pub Date : 2026-01-01DOI: 10.1016/j.jcmg.2025.09.001
Leslee J. Shaw PhD , Ron Blankstein MD , Jonathon A. Leipsic MD , Partho P. Sengupta MD , Koen Nieman MD , Jeroen J. Bax MD, PhD , William A. Zoghbi MD , Y. Chandrashekhar MD
{"title":"Clinical Integration of AI-Enabled Plaque Quantification to Improve Cardiovascular Risk Stratification","authors":"Leslee J. Shaw PhD , Ron Blankstein MD , Jonathon A. Leipsic MD , Partho P. Sengupta MD , Koen Nieman MD , Jeroen J. Bax MD, PhD , William A. Zoghbi MD , Y. Chandrashekhar MD","doi":"10.1016/j.jcmg.2025.09.001","DOIUrl":"10.1016/j.jcmg.2025.09.001","url":null,"abstract":"","PeriodicalId":14767,"journal":{"name":"JACC. Cardiovascular imaging","volume":"19 1","pages":"Pages 61-63"},"PeriodicalIF":15.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145032152","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}
Pub Date : 2026-01-01DOI: 10.1016/j.jcmg.2025.08.011
Mostafa A. Al-Alusi MD, MS , Emily S. Lau MD, MPH , Aeron M. Small MD, MTR , Christopher Reeder PhD , Tal Shnitzer PhD , Carl T. Andrews MS , Shinwan Kany MD , Joel T. Rämö MD , Julian S. Haimovich MD , Shaan Khurshid MD, MPH , Danita Y. Sanborn MD, MMSc , Michael H. Picard MD , Jennifer E. Ho MD , Mahnaz Maddah PhD , Patrick T. Ellinor MD, PhD
Background
Mitral valve prolapse (MVP) has a prevalence of 2% to 3% and increases risk of heart failure and sudden death, but diagnosis by transthoracic echocardiography requires time and expertise.
Objectives
This study aims to develop a deep learning model DROID-MVP (Dimensional Reconstruction of Imaging Data–Mitral Valve Prolapse) to classify MVP from digital echocardiogram videos.
Methods
DROID-MVP was trained and validated using 1,043,893 echocardiogram videos (48,829 studies) from 16,902 cardiology patients at MGH (Massachusetts General Hospital), and externally validated in 8,888 MGH primary care patients and 257 primary care patients at BWH (Brigham and Women’s Hospital). The authors tested associations among DROID-MVP predictions (range: 0-1), mitral regurgitation (MR) severity, and mitral valve repair or replacement (MVR).
Results
Of 16,902 patients (6,391 [38%] women; age 61 ± 16 years) in the derivation sample, 783 (4.6%) had MVP. DROID-MVP accurately identified MVP across the MGH cardiology internal validation set (area under the receiver-operating characteristic curve [AUROC]: 0.947 [95% CI: 0.910-0.984]; average precision [AP]: 0.682 [95% CI: 0.565-0.784]; prevalence: 0.036), MGH primary care external validation set (AUROC: 0.964 [95% CI: 0.951-0.977]; AP: 0.651 [95% CI: 0.578-0.716]; prevalence: 0.022), and BWH primary care external validation set (AUROC: 0.968 [95% CI: 0.946-0.989]; AP: 0.774 [95% CI: 0.666-0.797]; prevalence: 0.113). A high (>0.67) vs low (<0.33) DROID-MVP score was associated with moderate or severe MR (adjusted OR: 2.0 [95% CI: 1.1-3.8]; P = 0.030) and future MVR (adjusted HR: 3.7 [95% CI: 1.5-8.9]; P = 0.004).
Conclusions
A deep learning model identifies MVP from echocardiogram videos, and model predictions are associated with clinical endpoints including MR and future MVR. Deep learning can automate MVP diagnosis and potentially generate digital markers of clinically significant MVP.
背景：二尖瓣脱垂（MVP）的患病率为2%至3%，并增加心力衰竭和猝死的风险，但经胸超声心动图诊断需要时间和专业知识。目的建立深度学习模型DROID-MVP （Dimensional Reconstruction of Imaging data -二尖瓣脱垂），对数字超声心动图视频中的二尖瓣脱垂进行分类。方法sdroid - mvp通过来自麻省总医院（MGH） 16902名心脏病患者的1,043,893个超声心动图视频（48,829项研究）进行培训和验证，并在8888名MGH初级保健患者和257名BWH （Brigham and Women Hospital）初级保健患者中进行外部验证。作者测试了DROID-MVP预测（范围：0-1）、二尖瓣反流（MR）严重程度和二尖瓣修复或置换（MVR）之间的关系。结果在衍生样本的16902例患者中（6391例（38%）女性，年龄61±16岁），783例（4.6%）有MVP。DROID-MVP准确识别了MGH心内科内部验证集（患者工作特征曲线下面积[AUROC]: 0.947 [95% CI: 0.910-0.984]，平均精度[AP]: 0.682 [95% CI: 0.565-0.784]，患病率：0.036），MGH初级保健外部验证集（AUROC: 0.964 [95% CI: 0.951-0.977], AP: 0.651 [95% CI: 0.578-0.716]，患病率：0.022），BWH初级保健外部验证集(AUROC: 0.968 [95% CI: 0.946-0.989], AP: 0.774 [95% CI: 0.666-0.797]；流行:0.113)。高（>0.67）vs低（<0.33）DROID-MVP评分与中度或重度MR（校正or: 2.0 [95% CI: 1.1-3.8]; P = 0.030）和未来MVR（校正HR: 3.7 [95% CI: 1.5-8.9]; P = 0.004）相关。结论深度学习模型从超声心动图视频中识别MVP，模型预测与临床终点相关，包括MR和未来MVR。深度学习可以自动诊断MVP，并可能生成具有临床意义的MVP的数字标记。
{"title":"A Deep Learning Model to Identify Mitral Valve Prolapse From the Echocardiogram","authors":"Mostafa A. Al-Alusi MD, MS , Emily S. Lau MD, MPH , Aeron M. Small MD, MTR , Christopher Reeder PhD , Tal Shnitzer PhD , Carl T. Andrews MS , Shinwan Kany MD , Joel T. Rämö MD , Julian S. Haimovich MD , Shaan Khurshid MD, MPH , Danita Y. Sanborn MD, MMSc , Michael H. Picard MD , Jennifer E. Ho MD , Mahnaz Maddah PhD , Patrick T. Ellinor MD, PhD","doi":"10.1016/j.jcmg.2025.08.011","DOIUrl":"10.1016/j.jcmg.2025.08.011","url":null,"abstract":"<div><h3>Background</h3><div>Mitral valve prolapse (MVP) has a prevalence of 2% to 3% and increases risk of heart failure and sudden death, but diagnosis by transthoracic echocardiography requires time and expertise.</div></div><div><h3>Objectives</h3><div>This study aims to develop a deep learning model DROID-MVP (Dimensional Reconstruction of Imaging Data–Mitral Valve Prolapse) to classify MVP from digital echocardiogram videos.</div></div><div><h3>Methods</h3><div>DROID-MVP was trained and validated using 1,043,893 echocardiogram videos (48,829 studies) from 16,902 cardiology patients at MGH (Massachusetts General Hospital), and externally validated in 8,888 MGH primary care patients and 257 primary care patients at BWH (Brigham and Women’s Hospital). The authors tested associations among DROID-MVP predictions (range: 0-1), mitral regurgitation (MR) severity, and mitral valve repair or replacement (MVR).</div></div><div><h3>Results</h3><div>Of 16,902 patients (6,391 [38%] women; age 61 ± 16 years) in the derivation sample, 783 (4.6%) had MVP. DROID-MVP accurately identified MVP across the MGH cardiology internal validation set (area under the receiver-operating characteristic curve [AUROC]: 0.947 [95% CI: 0.910-0.984]; average precision [AP]: 0.682 [95% CI: 0.565-0.784]; prevalence: 0.036), MGH primary care external validation set (AUROC: 0.964 [95% CI: 0.951-0.977]; AP: 0.651 [95% CI: 0.578-0.716]; prevalence: 0.022), and BWH primary care external validation set (AUROC: 0.968 [95% CI: 0.946-0.989]; AP: 0.774 [95% CI: 0.666-0.797]; prevalence: 0.113). A high (>0.67) vs low (<0.33) DROID-MVP score was associated with moderate or severe MR (adjusted OR: 2.0 [95% CI: 1.1-3.8]; <em>P =</em> 0.030) and future MVR (adjusted HR: 3.7 [95% CI: 1.5-8.9]; <em>P =</em> 0.004).</div></div><div><h3>Conclusions</h3><div>A deep learning model identifies MVP from echocardiogram videos, and model predictions are associated with clinical endpoints including MR and future MVR. Deep learning can automate MVP diagnosis and potentially generate digital markers of clinically significant MVP.</div></div>","PeriodicalId":14767,"journal":{"name":"JACC. Cardiovascular imaging","volume":"19 1","pages":"Pages 18-29"},"PeriodicalIF":15.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145194449","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}
Pub Date : 2026-01-01DOI: 10.1016/j.jcmg.2025.08.008
Ali Ali MD, William R. Miranda MD, Christopher Francois MD, Sara ElZalabany MBBCh, Amr Moustafa MBBCh, Heidi M. Connolly MD, Alexander C. Egbe MD, MPH, MS
{"title":"Predictors and Prognostic Implications of Progressive Systemic Ventricular Dysfunction in Adults With Fontan Palliation","authors":"Ali Ali MD, William R. Miranda MD, Christopher Francois MD, Sara ElZalabany MBBCh, Amr Moustafa MBBCh, Heidi M. Connolly MD, Alexander C. Egbe MD, MPH, MS","doi":"10.1016/j.jcmg.2025.08.008","DOIUrl":"10.1016/j.jcmg.2025.08.008","url":null,"abstract":"","PeriodicalId":14767,"journal":{"name":"JACC. Cardiovascular imaging","volume":"19 1","pages":"Pages 139-141"},"PeriodicalIF":15.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145068393","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}
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