Dual-Phase Cross-Modal Contrastive Learning for CMR-Guided ECG Representations for Cardiovascular Disease Assessment
Amsterdam University Medical Center（阿姆斯特丹大学医学中心）；University of Amsterdam（阿姆斯特丹大学）；ETH Zurich（苏黎世联邦理工学院）；Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center（放射医学与核医学系，阿姆斯特丹大学医学中心）；Department of Radiology, Mayo Clinic, Rochester（放射医学系，梅奥诊所，罗切斯特）
Laura Alvarez-Florez, Angel Bujalance-Gomez, Femke Raijmakers, Samuel Ruiperez-Campillo, Maarten Z. H. Kolk, Jesse Wiers, Julia Vogt, Erik J. Bekkers, Ivana Išg… 展开作者
Laura Alvarez-Florez, Angel Bujalance-Gomez, Femke Raijmakers, Samuel Ruiperez-Campillo, Maarten Z. H. Kolk, Jesse Wiers, Julia Vogt, Erik J. Bekkers, Ivana Išgum, Fleur V. Y. Tjong
备注：
Paper accepted at SPIE Medical Imaging 2026 Conference
Cardiac magnetic resonance imaging (CMR) offers detailed evaluation of cardiac structure and function, but its limited accessibility restricts use to selected patient populations. In contrast, the electrocardiogram (ECG) is ubiquitous and inexpensive, and provides rich information on cardiac electrical activity and rhythm, yet offers limited insight into underlying cardiac structure and mechanical function. To address this, we introduce a contrastive learning framework that improves the extraction of clinically relevant cardiac phenotypes from ECG by learning from paired ECG-CMR data. Our approach aligns ECG representations with 3D CMR volumes at end-diastole (ED) and end-systole (ES), with a dual-phase contrastive loss to anchor each ECG jointly with both cardiac phases in a shared latent space. Unlike prior methods limited to 2D CMR representations with or without a temporal component, our framework models 3D anatomy at both ED and ES phases as distinct latent representations, enabling flexible disentanglement of structural and functional cardiac properties. Using over 34,000 ECG-CMR pairs from the UK Biobank, we demonstrate improved extraction of image-derived phenotypes from ECG, particularly for functional parameters ($\uparrow$ 9.2\%), while improvements in clinical outcome prediction remained modest ($\uparrow$ 0.7\%). This strategy could enable scalable and cost-effective extraction of image-derived traits from ECG. The code for this research is publicly available.
ECGFlowCMR: Pretraining with ECG-Generated Cine CMR Helps Cardiac Disease Classification and Phenotype Prediction
School of Intelligence Science and Technology, Peking University（北京大学智能科学与技术学院）；Polytechnic Institute of Zhejiang University, Zhejiang University（浙江大学 polytechnic 院）；Institute of Computing Technology, University of the Chinese Academy of Sciences（中国科学院计算技术研究所）；National Institute of Health Data Science, Peking University（北京大学健康数据科学国家研究院）；Institute of Microelectronics, University of the Chinese Academy of Sciences（中国科学院微电子研究所）；Department of Cardiology, Zhejiang University（浙江大学心内科部）
Global and Local Contrastive Learning for Joint Representations from Cardiac MRI and ECG
Chair for AI in Healthcare and Medicine, Technical University of Munich (TUM) and TUM University Hospital, Munich, Germany（人工智能在医疗与医学中的研究中心，技术大学慕尼黑（TUM）及慕尼黑技术大学医院）；School of Medicine, Klinikum rechts der Isar, TUM, Germany（医学院，右岸克里克医院，TUM，德国）；Department of Computing, Imperial College London, UK（计算学院，伦敦帝国学院，英国）；Munich Center for Machine Learning (MCML), Munich, Germany（慕尼黑机器学习中心（MCML），慕尼黑，德国）
Automated Quality Controlled Analysis of 2D Phase Contrast Cardiovascular Magnetic Resonance Imaging
King's College London（伦敦国王学院）；Duke University（杜克大学）；Technical University Munich（慕尼黑工业大学）；Helmholtz Center Munich（慕尼黑亥姆霍兹中心）；Guy's and St Thomas' NHS Foundation Trust（盖伊和圣托马斯国民保健服务基金会信托）
Translating Electrocardiograms to Cardiac Magnetic Resonance Imaging Useful for Cardiac Assessment and Disease Screening: A Multi-Center Study
College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；Department of Vascular Surgery, The First Affiliated Hospital of Zhejiang University School of Medicine（浙江大学医学院附属第一医院血管外科）；Department of Cardiology, The First Affiliated Hospital, Zhejiang University School of Medicine（浙江大学医学院附属第一医院心内科）；Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine（浙江大学医学院附属第一医院放射科）；Department of Vascular Surgery, Quzhou People’s Hospital（衢州人民医院血管外科）；Department of Cardiology, The Second Affiliated Hospital of Zhejiang University School of Medicine（浙江大学医学院附属第二医院心内科）；China Ship Scientific Research Center（中国船舶科学研究院）；Guangdong Transtek Medical Electronics Co., Ltd.（广东 Transtek 医疗电子有限公司）
ECG-LLM: Foundation Model for ECG-Based Cardiac Reasoning
Technical University of Munich (TUM)（慕尼黑工业大学）；TUM University Hospital（慕尼黑工业大学附属医院）；Imperial College London（帝国理工学院）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）
Leveraging Cardiac Imaging to Improve ECG-Based Detection of Chagas Disease in Resource-Constrained Settings
Amsterdam University Medical Center（阿姆斯特丹大学医学中心）；University of Amsterdam（阿姆斯特丹大学）；ETH Zurich（苏黎世联邦理工学院）；Amsterdam Cardiovascular Sciences（阿姆斯特丹心血管科学研究院）
Automated Diagnosis of Cardiovascular Diseases from Cardiac Magnetic Resonance Imaging Using Deep Learning Models: A Review
UNSW Sydney（新南威尔士大学悉尼分校）；University of Granada（格拉纳达大学）；K. N. Toosi University of Technology（K. N. 图西理工大学）；Deakin University（迪肯大学）；University of Technology Sydney（悉尼科技大学）；University of Leicester（莱斯特大学）；University of Cambridge（剑桥大学）；The University of New South Wales（新南威尔士大学）；Macquarie University（麦考瑞大学）；Ngee Ann Polytechnic（义安理工学院）；Asia University（亚洲大学）；Singapore University of Social Sciences（新加坡社科大学）
Integrating electrocardiogram and fundus images for early detection of cardiovascular diseases
University of Petroleum and Energy Studies（石油与能源研究大学）；SRM University AP（SRM大学阿普拉分校）；Sri Kalpana Heart Care（斯里卡尔帕纳心脏护理中心）；Centre for High Impact Neuroscience and Translational Applications, TCG Crest（TCG Crest 高影响力神经科学与转化应用中心）；Ashwini Eye Care（阿什维尼眼科护理中心）
BAAI Cardiac Agent: An intelligent multimodal agent for automated reasoning and diagnosis of cardiovascular diseases from cardiac magnetic resonance imaging
Beijing Academy of Artificial Intelligence（北京智源人工智能研究院）；Department of Radiology, Beijing Anzhen Hospital, Beijing Institute of Heart, Lung & Vascular Diseases, Capital Medical University（首都医科大学附属北京安贞医院放射科，北京市心肺血管疾病研究所）；Department of MR, the First Affiliated Hospital, Henan Medical University（河南医科大学第一附属医院磁共振科）；Department of Cardiology, Clinical Center for Coronary Heart Disease, Beijing Institute of Heart, Lung and Blood Vessel Disease, Beijing Anzhen Hospital, Capital Medical University（首都医科大学附属北京安贞医院心内科，冠心病临床中心，北京市心肺血管疾病研究所）；Department of Cardiac Surgery, Beijing Anzhen Hospital, Institute of Heart, Lung and Vascular Diseases, Capital Medical University（首都医科大学附属北京安贞医院心脏外科，心肺血管疾病研究所）
Fully Automated Assessment of Cardiac Coverage in Cine Cardiovascular Magnetic Resonance Images using an Explainable Deep Visual Salient Region Detection Model
Multi-view Hybrid Graph Convolutional Network for Volume-to-mesh Reconstruction in Cardiovascular MRI
Institute for Signals, Systems, and Computational Intelligence, sinc(i) CONICET-UNL（信号、系统与计算智能研究所，sinc(i) CONICET-UNL）；Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), School of Computing, University of Leeds（生物医学计算成像与模拟技术中心（CISTIB），计算学院，莱斯特大学）；Christabel Pankhurst Institute, The University of Manchester（克里斯特贝尔·潘克赫斯特研究所，曼彻斯特大学）；Centre for Computational Imaging and Modelling in Medicine (CIMIM), Department of Computer Science, School of Engineering, and Division of Informatics Imaging and Data Science, School of Health Sciences, The University of Manchester（医学计算成像与建模中心（CIMIM），计算机科学系，工程学院，信息学成像与数据科学系，健康科学学院，曼彻斯特大学）；Medical Imaging Research Centre (MIRC), Department of Cardiovascular Sciences, KU Leuven（医学影像研究中心（MIRC），心血管科学系，鲁汶大学）
Enabling Ultra-Fast Cardiovascular Imaging Across Heterogeneous Clinical Environments with A Generalist Foundation Model and Multimodal Database
Imperial College London（帝国理工学院）；Xiamen University（厦门大学）；Fudan University（复旦大学）；Zhejiang University School of Medicine（浙江大学医学院）；Wuhan University（武汉大学）；Shanghai Jiaotong University School of Medicine（上海交通大学医学院）；Tongji University School of Medicine（同济大学医学院）；Nanjing Medical University（南京医科大学）；Charité-Universitätsmedizin Berlin（柏林夏里特医院）；Royal Brompton Hospital（皇家布朗普顿医院）；Yantai University（烟台大学）；Hong Kong Centre for Cerebro-cardiovascular Health Engineering（香港心脑血管健康工程中心）；The University of Texas Southwestern Medical Center（德克萨斯大学西南医学中心）；United Imaging Healthcare Group Co., Ltd.（联影医疗科技股份有限公司）；The Ohio State University（俄亥俄州立大学）；ShanghaiTech University（上海科技大学）；Shanghai Fuying Medical Technology Co., Ltd.（上海福盈医疗科技有限公司）；GE Healthcare（通用电气医疗集团）；University of Oxford（牛津大学）；The Hong Kong Polytechnic University（香港理工大学）；Technische Universität München（慕尼黑工业大学）；Delft University of Technology（代尔夫特理工大学）；Pontificia Universidad Católica de Chile（智利天主教大学）；King’s College London（伦敦国王学院）；Shanghai Jiao Tong University（上海交通大学）
MulViMotion: Shape-aware 3D Myocardial Motion Tracking from Multi-View Cardiac MRI
Imperial College London（帝国理工学院）；Technical University Munich（慕尼黑工业大学）；University of Edinburgh（爱丁堡大学）；MRC London Institute of Medical Sciences（MRC伦敦医学科学研究所）
Assessment of Clonal Hematopoiesis of Indeterminate Potential and Future Cardiomyopathy from Cardiac Magnetic Resonance Imaging using Deep Learning in a Cardio-oncology Population
Yale School of Medicine（耶鲁大学医学院）；University of Pennsylvania（宾夕法尼亚大学）
Cross-Modal Contrastive Learning of ECG and Angiography Representations for Severe Stenosis Classification
Chair for AI in Healthcare and Medicine, Technical University of Munich and TUM University Hospital（人工智能在医疗与医学中的研究所，慕尼黑技术大学及慕尼黑大学医院）；Department of Computing, Imperial College London（伦敦帝国理工学院计算机系）；Munich Center for Machine Learning (MCML), Munich, Germany（慕尼黑机器学习中心（MCML））；Department of Internal Medicine, TUM University Hospital（慕尼黑大学医院内科学系）
