In this paper, we propose an enhanced 3D myocardial strain estimation procedure, which combines complementary displacement information from multiple orientations of a single imaging modality (untagged CMR SSFP images). To estimate myocardial strain across the left ventricle, we register the sets of short-axis, four-chamber and two-chamber views via a 2D non-rigid registration algorithm implemented in a commercial software (Segment, Medviso). We then create a series of interpolating functions for the three orthogonal directions of motion and use them to deform a tetrahedral mesh representation of a patient-specific left ventricle. Additionally, we correct for overestimation of displacement by introducing a weighting scheme that is based on displacement along the long axis. The procedure was evaluated on the STACOM 2011 dataset containing CMR SSFP images for 16 healthy volunteers. We show increased accuracy in estimating the three strain components (radial, circumferential, longitudinal) compared to reported results in the challenge, for the imaging modality of interest (SSFP). Our peak strain estimates are also significantly closer to reported measurements from studies of a larger cohort in the literature and our own ground truth measurements using Segment Strain Analysis Module. Our proposed procedure provides a relatively fast and simple method to improve 2D tracking results, with the added flexibility in either deforming a reconstructed mesh model from other image modalities or using the built-in CMR mesh reconstruction procedure. Our, proposed scheme presents a deforming patient-specific model of the left ventricle, using the commonest imaging modality , routinely administered in clinical settings, without requiring additional or specialized imaging protocols.
Fuzhou University（福州大学）；University of Oxford（牛津大学）；Fudan University（复旦大学）；Zhongshan Hospital, Fudan University（复旦大学附属中山医院）；Shanghai Medical School, Fudan University（复旦大学上海医学院）；Shanghai Institute of Medical Imaging（上海医学影像研究所）
Myocardial pathology segmentation (MyoPS) is critical for the risk stratification and treatment planning of myocardial infarction (MI). Multi-sequence cardiac magnetic resonance (MS-CMR) images can provide valuable information. For instance, balanced steady-state free precession cine sequences present clear anatomical boundaries, while late gadolinium enhancement and T2-weighted CMR sequences visualize myocardial scar and edema of MI, respectively. Existing methods usually fuse anatomical and pathological information from different CMR sequences for MyoPS, but assume that these images have bee…
This work evaluates deep learning-based myocardial infarction (MI) quantification using Segment cardiovascular magnetic resonance (CMR) software. Segment CMR software incorporates the expectation-maximization, weighted intensity, a priori information (EWA) algorithm used to generate the infarct scar volume, infarct scar percentage, and microvascular obstruction percentage. Here, Segment CMR software segmentation algorithm is updated with semantic segmentation with U-net to achieve and evaluate fully automated or deep learning-based MI quantification. The direct observation of graphs and the nu…
Fudan University（复旦大学）；University of Oxford（牛津大学）；Zhongshan Hospital, Fudan University（复旦大学附属中山医院）；Shanghai Medical School, Fudan University（复旦大学上海医学院）；Shanghai Institute of Medical Imaging（上海医学影像研究所）
Myocardial pathology segmentation (MyoPS) can be a prerequisite for the accurate diagnosis and treatment planning of myocardial infarction. However, achieving this segmentation is challenging, mainly due to the inadequate and indistinct information from an image. In this work, we develop an end-to-end deep neural network, referred to as MyoPS-Net, to flexibly combine five-sequence cardiac magnetic resonance (CMR) images for MyoPS. To extract precise and adequate information, we design an effective yet flexible architecture to extract and fuse cross-modal features. This architecture can tackle …
National University of Singapore（新加坡国立大学）；National University Heart Centre（新加坡国立大学心脏中心）
Automatic segmentation of myocardium in Late Gadolinium Enhanced (LGE) Cardiac MR (CMR) images is often difficult due to the intensity heterogeneity resulting from accumulation of contrast agent in infarcted areas. In this paper, we propose an automatic segmentation framework that fully utilizes shared information between corresponding cine and LGE images of a same patient. Given myocardial contours in cine CMR images, the proposed framework achieves accurate segmentation of LGE CMR images in a coarse-to-fine manner. Affine registration is first performed between the corresponding cine and LGE…
Southeast University（东南大学）；Shenzhen University（深圳大学）；Fudan University（复旦大学）；Shanghai Jiao Tong University（上海交通大学）；Kings College London（伦敦国王学院）；Affiliated Drum Tower Hospital of Nanjing University Medical School（南京大学医学院附属鼓楼医院）
Multi-sequence cardiac magnetic resonance (CMR) provides essential pathology information (scar and edema) to diagnose myocardial infarction. However, automatic pathology segmentation can be challenging due to the difficulty of effectively exploring the underlying information from the multi-sequence CMR data. This paper aims to tackle the scar and edema segmentation from multi-sequence CMR with a novel auto-weighted supervision framework, where the interactions among different supervised layers are explored under a task-specific objective using reinforcement learning. Furthermore, we design a c…
National Heart & Lung Institute, Imperial College London（伦敦帝国理工学院国家心肺研究所）；Royal Brompton Hospital（皇家布朗普顿医院）
Four-dimensional (4D) left ventricular myocardial velocity mapping (MVM) is a cardiac magnetic resonance (CMR) technique that allows assessment of cardiac motion in three orthogonal directions. Accurate and reproducible delineation of the myocardium is crucial for accurate analysis of peak systolic and diastolic myocardial velocities. In addition to the conventionally available magnitude CMR data, 4D MVM also acquires three velocity-encoded phase datasets which are used to generate velocity maps. These can be used to facilitate and improve myocardial delineation. Based on the success of deep l…
Delayed-enhancement cardiac magnetic resonance (DE-CMR)provides important diagnostic and prognostic information on myocardial viability. The presence and extent of late gadolinium enhancement (LGE)in DE-CMR is negatively associated with the probability of improvement in left ventricular function after revascularization. Moreover, LGE findings can support the diagnosis of several other cardiomyopathies, but their absence does not rule them out, making disease classification by visual assessment difficult. In this work, we propose deep learning neural networks that can automatically predict myoc…
CineMyoPS: Segmenting Myocardial Pathologies from Cine Cardiac MR
School of Medical Imaging, Fujian Medical University（福建医科大学医学影像学院）；College of Physics and Information Engineering, Fuzhou University（福州大学物理与信息工程学院）；Department of Biomedical Engineering, National University of Singapore（新加坡国立大学生物医学工程系）；School of Data Science, Fudan University（复旦大学数据科学学院）；Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University（上海交通大学医学院仁济医院放射科）
University of Auckland（奥克兰大学）；Queen Mary University of London（伦敦玛丽女王大学）；University of Oxford（牛津大学）
Purpose: To demonstrate the feasibility and performance of a fully automated deep learning framework to estimate myocardial strain from short-axis cardiac magnetic resonance tagged images. Methods and Materials: In this retrospective cross-sectional study, 4508 cases from the UK Biobank were split randomly into 3244 training and 812 validation cases, and 452 test cases. Ground truth myocardial landmarks were defined and tracked by manual initialization and correction of deformable image registration using previously validated software with five readers. The fully automatic framework consisted …
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）
Recovering the 3D motion of the heart from cine cardiac magnetic resonance (CMR) imaging enables the assessment of regional myocardial function and is important for understanding and analyzing cardiovascular disease. However, 3D cardiac motion estimation is challenging because the acquired cine CMR images are usually 2D slices which limit the accurate estimation of through-plane motion. To address this problem, we propose a novel multi-view motion estimation network (MulViMotion), which integrates 2D cine CMR images acquired in short-axis and long-axis planes to learn a consistent 3D motion fi…
Improve Myocardial Strain Estimation based on Deformable Groupwise Registration with a Locally Low-Rank Dissimilarity Metric
Shanghai Jiao Tong University（上海交通大学）；School of Biomedical Engineering（生物医学工程学院）；National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy（国家医学诊疗先进磁共振技术工程研究中心）；Renji Hospital, School of Medicine, Shanghai Jiao Tong University（上海交通大学医学院附属仁济医院）
The University of Hong Kong（香港大学）；Imperial College London（伦敦帝国理工学院）；Royal Brompton Hospital（皇家布朗普顿医院）
Myocardial Velocity Mapping Cardiac MR (MVM-CMR) can be used to measure global and regional myocardial velocities with proved reproducibility. Accurate left ventricle delineation is a prerequisite for robust and reproducible myocardial velocity estimation. Conventional manual segmentation on this dataset can be time-consuming and subjective, and an effective fully automated delineation method is highly in demand. By leveraging recently proposed deep learning-based semantic segmentation approaches, in this study, we propose a novel fully automated framework incorporating a 3D-UNet backbone arch…
Fudan University（复旦大学）；Shanghai Jiao Tong University（上海交通大学）；Universitat de Barcelona（巴塞罗那大学）；Universitat Autònoma de Barcelona（巴塞罗那自治大学）；University of Electronic Science and Technology of China（电子科技大学）；Northwestern Polytechnical University（西北工业大学）；Hunan University（湖南大学）；National Engineering Laboratory for Robot Visual Perception and Control Technology（机器人视觉感知与控制技术国家工程实验室）；Fuzhou University（福州大学）；Comprehensive Heart Failure Center（综合心力衰竭中心）
Assessment of myocardial viability is essential in diagnosis and treatment management of patients suffering from myocardial infarction, and classification of pathology on myocardium is the key to this assessment. This work defines a new task of medical image analysis, i.e., to perform myocardial pathology segmentation (MyoPS) combining three-sequence cardiac magnetic resonance (CMR) images, which was first proposed in the MyoPS challenge, in conjunction with MICCAI 2020. The challenge provided 45 paired and pre-aligned CMR images, allowing algorithms to combine the complementary information fr…
Coronary artery disease (CAD) remains the world's leading cause of mortality and the disease burden is continually expanding as the population ages. Recently, the MR-INFORM randomised trial has demonstrated that the management of patients with stable CAD can be guided by stress perfusion cardiovascular magnetic resonance (CMR) imaging and it is non-inferior to the using the invasive reference standard of fractional flow reserve. The benefits of using stress perfusion CMR include that it is non-invasive and significantly reduces the number of unnecessary coronary revascularisations. As compared…
University of Virginia（弗吉尼亚大学）；University of Virginia Health System（弗吉尼亚大学卫生系统）
Automated identification of myocardial scar from late gadolinium enhancement cardiac magnetic resonance images (LGE-CMR) is limited by image noise and artifacts such as those related to motion and partial volume effect. This paper presents a novel joint deep learning (JDL) framework that improves such tasks by utilizing simultaneously learned myocardium segmentations to eliminate negative effects from non-region-of-interest areas. In contrast to previous approaches treating scar detection and myocardium segmentation as separate or parallel tasks, our proposed method introduces a message passin…
University of Alberta（阿尔伯塔大学）；Mazankowski Alberta Heart Institute（马赞科夫斯基阿尔伯塔心脏研究所）
Myocardial characterization is essential for patients with myocardial infarction and other myocardial diseases, and the assessment is often performed using cardiac magnetic resonance (CMR) sequences. In this study, we propose a fully automated approach using deep convolutional neural networks (CNN) for cardiac pathology segmentation, including left ventricular (LV) blood pool, right ventricular blood pool, LV normal myocardium, LV myocardial edema (ME) and LV myocardial scars (MS). The input to the network consists of three CMR sequences, namely, late gadolinium enhancement (LGE), T2 and balan…
Myocardial Strain Drift Correction in Deep Learning Based Ultrasound Tracking
University of Sherbrooke（舍布鲁克大学）；INSA（法国国立应用科学学院）；Université Lyon 1（里昂第一大学）；CREATIS（CREATIS实验室）；Institut Universitaire de France (IUF)（法国大学研究院（IUF））；NTNU（挪威科技大学）；St. Olavs Hospital（圣奥拉夫医院）；SINTEF Digital（SINTEF数字研究所）；Levanger Hospital（莱旺厄尔医院）；Nord-Trøndelag Hospital Trust（北特伦德拉格医院信托）；Hôpital Croix-Rousse（克鲁瓦鲁斯医院）；Hospices Civils de Lyon（里昂民用医院集团）；Hôpital Lyon Sud（里昂南部医院）
College of Physics and Information Engineering, Fuzhou University（福州大学物理与信息工程学院）；School of Basic Medical Science, Fudan University（复旦大学基础医学院）
Multi-sequence of cardiac magnetic resonance (CMR) images can provide complementary information for myocardial pathology (scar and edema). However, it is still challenging to fuse these underlying information for pathology segmentation effectively. This work presents an automatic cascade pathology segmentation framework based on multi-modality CMR images. It mainly consists of two neural networks: an anatomical structure segmentation network (ASSN) and a pathological region segmentation network (PRSN). Specifically, the ASSN aims to segment the anatomical structure where the pathology may exis…
Brigham and Women's Hospital（布莱根妇女医院）；Harvard Medical School（哈佛医学院）；Bariloche Atomic Center（巴里洛切原子中心）；CONICET（阿根廷国家科学与技术研究委员会）；Instituto Balseiro（巴尔塞罗研究所）；Universidad Nacional de Cuyo（库约国立大学）；Sanatorio San Carlos（圣卡洛斯疗养院）；INTECNUS（INTECNUS研究所）；CNEA – Comisión Nacional de Energía Atómica（阿根廷国家原子能委员会）
Background: Artificial intelligence techniques have demonstrated great potential in cardiology, especially to detect imperceptible patterns for the human eye. In this sense, these techniques seem to be adequate to identify patterns in the myocardial texture which could lead to characterize and quantify fibrosis. Purpose: The aim of this study was to postulate a new artificial intelligence method to identify fibrosis in cine cardiac magnetic resonance (CMR) imaging. Methods: A retrospective observational study was carried out in a population of 75 subjects from a clinical center of San Carlos d…
University of Minnesota（明尼苏达大学）；Center for Magnetic Resonance Research, University of Minnesota（明尼苏达大学磁共振研究中心）；Beth Israel Deaconess Medical Center, Harvard Medical School（哈佛医学院贝斯以色列女执事医疗中心）
Late gadolinium enhancement (LGE) cardiac MRI (CMR) is the clinical standard for diagnosis of myocardial scar. 3D isotropic LGE CMR provides improved coverage and resolution compared to 2D imaging. However, image acceleration is required due to long scan times and contrast washout. Physics-guided deep learning (PG-DL) approaches have recently emerged as an improved accelerated MRI strategy. Training of PG-DL methods is typically performed in supervised manner requiring fully-sampled data as reference, which is challenging in 3D LGE CMR. Recently, a self-supervised learning approach was propose…
National University of Singapore（新加坡国立大学）；National University Heart Centre（新加坡国立大学心脏中心）；National University Hospital（新加坡国立大学医院）
Automatic segmentation of the left ventricle (LV) in late gadolinium enhanced (LGE) cardiac MR (CMR) images is difficult due to the intensity heterogeneity arising from accumulation of contrast agent in infarcted myocardium. In this paper, we present a comprehensive framework for automatic 3D segmentation of the LV in LGE CMR images. Given myocardial contours in cine images as a priori knowledge, the framework initially propagates the a priori segmentation from cine to LGE images via 2D translational registration. Two meshes representing respectively endocardial and epicardial surfaces are the…
Fudan University（复旦大学）；Shanghai Jiao Tong University（上海交通大学）；Imperial College London（帝国理工学院）；Universitat de Barcelona（巴塞罗那大学）；Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）；Harbin Institute of Technology（哈尔滨工业大学）；Southern University of Science and Technology（南方科技大学）；Technical University of Munich（慕尼黑工业大学）；INRIA（法国国家信息与自动化研究所）；Université Côte d’Azur（蔚蓝海岸大学）；NVIDIA（英伟达）；Xiamen University（厦门大学）；Sichuan University（四川大学）；Tencent AI Lab（腾讯人工智能实验室）
Accurate computing, analysis and modeling of the ventricles and myocardium from medical images are important, especially in the diagnosis and treatment management for patients suffering from myocardial infarction (MI). Late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) provides an important protocol to visualize MI. However, automated segmentation of LGE CMR is still challenging, due to the indistinguishable boundaries, heterogeneous intensity distribution and complex enhancement patterns of pathological myocardium from LGE CMR. Furthermore, compared with the other sequences LG…
School of Engineering, University of Edinburgh（爱丁堡大学工程学院）；Center for Cardiovascular Science, University of Edinburgh（爱丁堡大学心血管科学中心）；The Alan Turing Institute（艾伦·图灵研究所）
Automatic segmentation of multi-sequence (multi-modal) cardiac MR (CMR) images plays a significant role in diagnosis and management for a variety of cardiac diseases. However, the performance of relevant algorithms is significantly affected by the proper fusion of the multi-modal information. Furthermore, particular diseases, such as myocardial infarction, display irregular shapes on images and occupy small regions at random locations. These facts make pathology segmentation of multi-modal CMR images a challenging task. In this paper, we present the Max-Fusion U-Net that achieves improved path…
Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）；University of Leeds（利兹大学）
Left ventricle segmentation and morphological assessment are essential for improving diagnosis and our understanding of cardiomyopathy, which in turn is imperative for reducing risk of myocardial infarctions in patients. Convolutional neural network (CNN) based methods for cardiac magnetic resonance (CMR) image segmentation rely on supervision with pixel-level annotations, and may not generalize well to images from a different domain. These methods are typically sensitive to variations in imaging protocols and data acquisition. Since annotating multi-sequence CMR images is tedious and subject …
University of Leeds（利兹大学）；University of Oxford（牛津大学）；Queen Mary University of London（伦敦玛丽女王大学）；Barts Health NHS Trust（巴茨健康国民保健制度信托基金）
Large prospective epidemiological studies acquire cardiovascular magnetic resonance (CMR) images for pre-symptomatic populations and follow these over time. To support this approach, fully automatic large-scale 3D analysis is essential. In this work, we propose a novel deep neural network using both CMR images and patient metadata to directly predict cardiac shape parameters. The proposed method uses the promising ability of statistical shape models to simplify shape complexity and variability together with the advantages of convolutional neural networks for the extraction of solid visual feat…
MorphiNet: A Graph Subdivision Network for Adaptive Bi-ventricle Surface Reconstruction
School of Biomedical Engineering and Imaging Sciences, King’s College London（生物医学工程与影像科学学院，伦敦国王学院）；Brain Imaging & Neuro Epidemiology Department, Luxemboug Institute of Health（脑影像与神经流行病学部门，卢森堡健康研究所）；Department of Biostatistics and Health Informatics, King’s College London（生物统计学与健康信息学系，伦敦国王学院）
Extreme Cardiac MRI Analysis under Respiratory Motion: Results of the CMRxMotion Challenge
Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, Shanghai 200032, China；Shanghai Key Laboratory of MICCAI, Fudan University, Shanghai, Shanghai 200032, China；Department of Electrical；Electronic Engineering \& I-X, Imperial College London, London, London SW7 2AZ, United Kingdom；Department of Radiology, Zhongshan Hospital Affiliated to Fudan University, Shanghai, Shanghai 200032, China；Department of Computing, Imperial College London, London, London SW7 2AZ, United Kingdom；School of Computer Science, University of Sheffield, Sheffield, S1 4DP, United Kingdom；Department of Engineering Science, University of Oxford, Oxford, OX2 0ES, United Kingdom；Shanghai Pudong Hospital；Human Phenome Institute, Fudan University, Shanghai, 201203, China；School of Computer Science, University of Nottingham, Nottingham, NG8 1BB, United Kingdom；Diagnostic Imaging, University of Alberta, Edmonton, AB T6G 1K4, Canada；Department of Computer Science；Engineering, The Chinese University of Hong Kong, Hong Kong, Hong Kong 000000, China；The D-Lab, Department of Precision Medicine, GROW - Research Institute for Oncology；Reproduction, Maastricht University, 6220 MD Maastricht, The Netherlands；Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven 5612 AZ, The Netherlands；Department of Radiology, Northwestern University, 737 N. Michigan Ave, Suite 1600, Chicago 60611, United States；United Imaging Research, 393 Middle Huaxia Road, Pudong, Shanghai 201210, China；Division of Image Processing, Department of Radiology, Leiden University Medical Center, PO Box 9600, Leiden 2300 RC, The Netherlands；Université Bourgogne Europe, CNRS, ICMUB UMR 6302, 21000 Dijon, France；Istanbul Technical University, Maslak, 34467, İstanbul, Türkiye；Computer Science, Technical University of Darmstadt, Karolinenpl. 5, 64289 Darmstadt, Germany；Lung Institute, Faculty of Medicine, Imperial College London, Guy Scadding Building, Cale Street, London, SW3 6LY,United Kingdom；Hawkes Institute, Department of Computer Science, University College London, 66-72 Gower St, London, United Kingdom；School of Medicine, University College Dublin, Belfield, Dublin, D04 V1W8, Ireland；CeADAR: Ireland's Centre for AI, University College Dublin, Belfield, Dublin, D04 V1W8, Ireland；Sano Centre for Computational Medicine, Czarnowiejska 36, 30-054, Krakow, Poland；Faculty of Mathematics；Computer Science, Jagiellonian University, S. Łojasiewicza 6, Krakow, Poland；Department of Electronic；Computer Engineering, The Hong Kong University of Science；School of Instrument Science；Engineering, Southeast University, Nanjing, Nanjing 210096, China；College of Artificial Intelligence, Nanjing University of Aeronautics；Academy for Engineering；Technology, Fudan University, Shanghai, Shanghai 200433, China；College of Biomedical Engineering, Fudan University, Shanghai, Shanghai 200433, China；Institute of Science；Technology for Brain-inspired Intelligence, Fudan University, Shanghai, Shanghai 200433, China；Department of Brain Sciences, Imperial College London, London, London SW7 2AZ, United Kingdom；Data Science Institute, Imperial College London, London, London SW7 2AZ, United Kingdom
King’s College London（伦敦国王学院）；University of Michigan（密歇根大学）
Valve annuli motion and morphology, measured from non-invasive imaging, can be used to gain a better understanding of healthy and pathological heart function. Measurements such as long-axis strain as well as peak strain rates provide markers of systolic function. Likewise, early and late-diastolic filling velocities are used as indicators of diastolic function. Quantifying global strains, however, requires a fast and precise method of tracking long-axis motion throughout the cardiac cycle. Valve landmarks such as the insertion of leaflets into the myocardial wall provide features that can be t…
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）
Deep Learning Strain Estimation: Is Physics-Based Simulation the Solution?
Dept. of Computer Science, University of Sherbrooke（计算机科学系， Sherbrooke 大学）；INSA, Université Lyon 1, CNRS UMR 5220, Inserm U1206, CREATIS（INSA，里昂 1 大学，CNRS UMR 5220，Inserm U1206，CREATIS）；Institut Universitaire de France (IUF)（法国国家研究院（IUF））；Cardiology Dept., Hôpital Croix-Rousse, Hospices Civils de Lyon（里昂医院心血管科，Hospices Civils de Lyon）；Cardiology Dept., Hôpital Lyon Sud, Hospices Civils de Lyon（里昂南部医院心血管科，Hospices Civils de Lyon）；Dept. of Computer Science, Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology (NTNU)（计算机科学系，信息科技与电气工程学院，挪威科学技术大学（NTNU））；Dept. of Circulation and Medical Imaging, NTNU（循环医学与医学影像系，NTNU）；Department of Medicine, Hospital of Southern Norway, Arendal, Norway（南部挪威医院医学部，Arendal，挪威）；Dept. of Cardiology and Cardiothoracic Surgery, St. Olavs Hospital, Trondheim, Norway（心内科和心胸外科部，St. Olavs 医院，Trondheim，挪威）；Dept. of Health Research, SINTEF Digital, Trondheim, Norway（健康研究部，SINTEF 数字技术，Trondheim，挪威）；Dept. of Medicine, Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway（医学部，Levanger 医院，Nord-Trøndelag 医院信托，Levanger，挪威）；Dept. of Cardiology, Oslo University Hospital, Rikshospitalet and the Faaculty of Medicine, University of Oslo, Norway（心内科，奥斯陆大学医院 Rikshospitalet，奥斯陆大学医学院，挪威）
