Echocardiography to Cardiac MRI View Transformation for Real-Time Blind Restoration
Tampere University（坦佩雷大学）；Qatar University（卡塔尔大学）；VTT Technical Research Centre of Finland（芬兰国家技术研究中心）；Hamad Medical Corporation Hospital（哈马德医疗公司医院）；Hayatabad Medical Complex（海亚特阿巴德医疗中心）
Echocardiography is the most widely used imaging to monitor cardiac functions, serving as the first line in early detection of myocardial ischemia and infarction. However, echocardiography often suffers from several artifacts including sensor noise, lack of contrast, severe saturation, and missing myocardial segments which severely limit its usage in clinical diagnosis. In recent years, several machine learning methods have been proposed to improve echocardiography views. Yet, these methods usually address only a specific problem (e.g. denoising) and thus cannot provide a robust and reliable restoration in general. On the other hand, cardiac MRI provides a clean view of the heart without suffering such severe issues. However, due to its significantly higher cost, it is often only afforded by a few major hospitals, hence hindering its use and accessibility. In this pilot study, we propose a novel approach to transform echocardiography into the cardiac MRI view. For this purpose, Echo2MRI dataset, consisting of echocardiography and real cardiac MRI image pairs, is composed and will be shared publicly. A dedicated Cycle-consistent Generative Adversarial Network (Cycle-GAN) is trained to learn the transformation from echocardiography frames to cardiac MRI views. An extensive set of qualitative evaluations shows that the proposed transformer can synthesize high-quality artifact-free synthetic cardiac MRI views from a given sequence of echocardiography frames. Medical evaluations performed by a group of cardiologists further demonstrate that synthetic MRI views are indistinguishable from their original counterparts and are preferred over their initial sequence of echocardiography frames for diagnosis in 78.9% of the cases.
University of British Columbia（不列颠哥伦比亚大学）；Vancouver General Hospital（温哥华综合医院）
Transthoracic echo is one of the most common means of cardiac studies in the clinical routines. During the echo exam, the sonographer captures a set of standard cross sections (echo views) of the heart. Each 2D echo view cuts through the 3D cardiac geometry via a unique plane. Consequently, different views share some limited information. In this work, we investigate the feasibility of generating a 2D echo view using another view based on adversarial generative models. The objective optimized to train the view-conversion model is based on the ideas introduced by LSGAN, PatchGAN and Conditional …
Echocardiography (echo) is a common means of evaluating cardiac conditions. Due to the label scarcity, semi-supervised paradigms in automated echo analysis are getting traction. One of the most sought-after problems in echo is the segmentation of cardiac structures (e.g. chambers). Accordingly, we propose an echocardiogram generation approach using generative adversarial networks with a conditional patch-based discriminator. In this work, we validate the feasibility of GAN-enhanced echo generation with different conditions (segmentation masks), namely, the left ventricle, ventricular myocardiu…
Latent Motion Profiling for Annotation-free Cardiac Phase Detection in Adult and Fetal Echocardiography Videos
Department of Engineering Science, University of Oxford（牛津大学工程科学系）；Hong Kong Centre for Cerebro-Cardiovascular Health Engineering（香港脑心血管健康工程中心）；Department of Maternal and Child Health and Urological Sciences, Sapienza University of Rome（罗马萨皮恩扎大学妇幼保健与泌尿科学系）；Nuffield Department of Women’s and Reproductive Health, University of Oxford（牛津大学努尔菲尔德妇女与生殖健康系）
Orientation-Robust Latent Motion Trajectory Learning for Annotation-free Cardiac Phase Detection in Fetal Echocardiography
Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford（生物医学工程研究所，工程科学系，牛津大学）；Nuffield Department of Women’s and Reproductive Health, University of Oxford（妇女与生殖健康纽菲尔德部门，牛津大学）
Automatic Cardiac Pathology Recognition in Echocardiography Images Using Higher Order Dynamic Mode Decomposition and a Vision Transformer for Small Datasets
Universidad Politécnica de Madrid（马德里理工大学）；Centro Nacional de Investigaciones Cardiovasculares (CNIC)（国家心血管研究中心）；Universidad Complutense de Madrid（马德里康普顿斯大学）；Center for Computational Simulation (CCS)（计算仿真中心）
China University of Petroleum（中国石油大学）；Qingdao Women and Children’s Hospital（青岛市妇女儿童医院）
Echocardiography is a powerful prenatal examination tool for early diagnosis of fetal congenital heart diseases (CHDs). The four-chamber (FC) view is a crucial and easily accessible ultrasound (US) image among echocardiography images. Automatic analysis of FC views contributes significantly to the early diagnosis of CHDs. The first step to automatically analyze fetal FC views is locating the fetal four crucial chambers of heart in a US image. However, it is a greatly challenging task due to several key factors, such as numerous speckles in US images, the fetal cardiac chambers with small size …
King's College London（伦敦国王学院）；Guy’s and St Thomas' Hospital（盖伊和圣托马斯医院）；University Medical Center Utrecht（乌得勒支大学医学中心）；Royal Brompton Hospital (Guy’s and St Thomas' NHS Foundation Trust)（皇家布朗普顿医院（盖伊和圣托马斯NHS基金会信托））
Left ventricular (LV) function is an important factor in terms of patient management, outcome, and long-term survival of patients with heart disease. The most recently published clinical guidelines for heart failure recognise that over reliance on only one measure of cardiac function (LV ejection fraction) as a diagnostic and treatment stratification biomarker is suboptimal. Recent advances in AI-based echocardiography analysis have shown excellent results on automated estimation of LV volumes and LV ejection fraction. However, from time-varying 2-D echocardiography acquisition, a richer descr…
Peng Cheng Laboratory（鹏城实验室）；Beihang University（北京航空航天大学）；Capital Medical University（首都医科大学）；Beijing Anzhen Hospital（北京安贞医院）；Beijing Advanced Innovation Center for Big Data-Based Precision Medicine（北京大数据精准医疗先进创新中心）
As an important scan plane, four chamber view is routinely performed in both second trimester perinatal screening and fetal echocardiographic examinations. The biometrics in this plane including cardio-thoracic ratio (CTR) and cardiac axis are usually measured by sonographers for diagnosing congenital heart disease. However, due to the commonly existing artifacts like acoustic shadowing, the traditional manual measurements not only suffer from the low efficiency, but also with the inconsistent results depending on the operators' skills. In this paper, we present an anchor-free ellipse detectio…
The University of Winnipeg（温尼伯大学）；Qatar University（卡塔尔大学）；Hamad Medical Corporation（哈马德医疗公司）
Cardiac imaging known as echocardiography is a non-invasive tool utilized to produce data including images and videos, which cardiologists use to diagnose cardiac abnormalities in general and myocardial infarction (MI) in particular. Echocardiography machines can deliver abundant amounts of data that need to be quickly analyzed by cardiologists to help them make a diagnosis and treat cardiac conditions. However, the acquired data quality varies depending on the acquisition conditions and the patient's responsiveness to the setup instructions. These constraints are challenging to doctors especi…
University of Sherbrooke（舍布鲁克大学）；Univ Lyon（里昂大学）；Université Claude Bernard Lyon 1（里昂第一大学）；UJM-Saint Etienne（圣艾蒂安大学）；CNRS（法国国家科学研究中心）；Inserm（法国国家健康与医学研究院）；Institut Universitaire de France（法国大学研究院）
Convolutional neural networks (CNN) have demonstrated their ability to segment 2D cardiac ultrasound images. However, despite recent successes according to which the intra-observer variability on end-diastole and end-systole images has been reached, CNNs still struggle to leverage temporal information to provide accurate and temporally consistent segmentation maps across the whole cycle. Such consistency is required to accurately describe the cardiac function, a necessary step in diagnosing many cardiovascular diseases. In this paper, we propose a framework to learn the 2D+time apical long-axi…
The University of British Columbia（不列颠哥伦比亚大学）；Vancouver General Hospital（温哥华综合医院）
In echocardiography (echo), an electrocardiogram (ECG) is conventionally used to temporally align different cardiac views for assessing critical measurements. However, in emergencies or point-of-care situations, acquiring an ECG is often not an option, hence motivating the need for alternative temporal synchronization methods. Here, we propose Echo-SyncNet, a self-supervised learning framework to synchronize various cross-sectional 2D echo series without any external input. The proposed framework takes advantage of both intra-view and inter-view self supervisions. The former relies on spatiote…
Cardiac valve event timing in echocardiography using deep learning and triplane recordings
GE Vingmed Ultrasound；Norwegian University of Science and Technology（挪威科技大学）；Oslo University Hospital, Rikshospitalet（奥斯陆大学医院（雷克沙医院））；University of Oslo（奥斯陆大学）；SINTEF Digital（SINTEF数字研究所）；St. Olavs hospital（圣奥拉夫斯医院）
We present a novel multimodal deep learning framework for cardiac resynchronisation therapy (CRT) response prediction from 2D echocardiography and cardiac magnetic resonance (CMR) data. The proposed method first uses the `nnU-Net' segmentation model to extract segmentations of the heart over the full cardiac cycle from the two modalities. Next, a multimodal deep learning classifier is used for CRT response prediction, which combines the latent spaces of the segmentation models of the two modalities. At inference time, this framework can be used with 2D echocardiography data only, whilst taking…
A Novel Data Augmentation Tool for Enhancing Machine Learning Classification: A New Application of the Higher Order Dynamic Mode Decomposition for Improved Cardiac Disease Identification
Universidad Politécnica de Madrid（马德里理工大学）；ETSI Aeronáutica y del Espacio（航空航天高级技术工程学院）；ETSI Telecomunicación（电信高级技术工程学院）；Universidad Complutense de Madrid（马德里康普顿斯大学）；Centro Nacional de Investigaciones Cardiovasculares (CNIC)（国家心血管研究中心）；Center for Computational Simulation (CCS)（计算仿真中心）
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）
Purpose: Echocardiography is commonly used as a non-invasive imaging tool in clinical practice for the assessment of cardiac function. However, delineation of the left ventricle is challenging due to the inherent properties of ultrasound imaging, such as the presence of speckle noise and the low signal-to-noise ratio. Methods: We propose a semi-automated segmentation algorithm for the delineation of the left ventricle in temporal 3D echocardiography sequences. The method requires minimal user interaction and relies on a diffeomorphic registration approach. Advantages of the method include no d…
Continuous 3D Myocardial Motion Tracking via Echocardiography
School of Electronic Science and Engineering, Nanjing University（南京大学电子科学与工程学院）；Medical School, Nanjing University（南京大学医学院）；Zhongshan Hospital, Fudan University（复旦大学中山医院）；College of Optical Sciences, University of Arizona（亚利桑那大学光学科学学院）
NVIDIA（英伟达）；Lenovo Research（联想研究院）；Piedmont Heart Institute（皮埃蒙特心脏研究所）；Tencent Medical AI Lab（腾讯医疗人工智能实验室）；University of California, Irvine（加州大学欧文分校）
Echocardiography has become routinely used in the diagnosis of cardiomyopathy and abnormal cardiac blood flow. However, manually measuring myocardial motion and cardiac blood flow from echocardiogram is time-consuming and error-prone. Computer algorithms that can automatically track and quantify myocardial motion and cardiac blood flow are highly sought after, but have not been very successful due to noise and high variability of echocardiography. In this work, we propose a neural multi-scale self-supervised registration (NMSR) method for automated myocardial and cardiac blood flow dense track…
A Unified Approach for Comprehensive Analysis of Various Spectral and Tissue Doppler Echocardiography
Ontact Health（Ontact健康公司）；Yonsei University College of Medicine（延世大学医学院）；CONNECT-AI Research Center, Yonsei University College of Medicine（延世大学医学院CONNECT-AI研究中心）；Seoul National University Bundang Hospital（首尔国立大学盆唐医院）；Severance Cardiovascular Hospital, Yonsei University Health System（延世大学医疗系统世福兰斯心血管医院）
Echo-POSED: Geometric Self-Distillation for Echocardiography Guidance
Medical Technology \& E-Health, Akershus University Hospital, Norway\ Department of Technology Systems, University of Oslo, Norway K.G. Jebsen Center for Cardiac Biomarkers, University of Oslo, Norway Department of Cardiology, Akershus University Hospital, Norway The Intervention Centre, Oslo University Hospital, Norway Department of Informatics, University of Oslo, Norway Correspondence: Shared first authorship
Continuous long-term monitoring of electrocardiography (ECG) signals is crucial for the early detection of cardiac abnormalities such as arrhythmia. Non-clinical ECG recordings acquired by Holter and wearable ECG sensors often suffer from severe artifacts such as baseline wander, signal cuts, motion artifacts, variations on QRS amplitude, noise, and other interferences. Usually, a set of such artifacts occur on the same ECG signal with varying severity and duration, and this makes an accurate diagnosis by machines or medical doctors extremely difficult. Despite numerous studies that have attem…
Delineation of the cardiac structures from 2D echocardiographic images is a common clinical task to establish a diagnosis. Over the past decades, the automation of this task has been the subject of intense research. In this paper, we evaluate how far the state-of-the-art encoder-decoder deep convolutional neural network methods can go at assessing 2D echocardiographic images, i.e segmenting cardiac structures as well as estimating clinical indices, on a dataset especially designed to answer this objective. We therefore introduce the Cardiac Acquisitions for Multi-structure Ultrasound Segmentat…
GHOST-CAT: An Efficient and Practical Network for Mesh Generation from 3D Echocardiography
Auckland Bioengineering Institute, University of Auckland（奥克兰大学生物工程研究所）；Faculty of Informatics, Telkom University（印尼电信大学信息学院）；School of Biomedical Engineering and Imaging Sciences, King’s College London（伦敦国王学院生物医学工程与成像科学系）；Department of Engineering Science and Biomedical Engineering, University of Auckland（奥克兰大学生命科学工程系）
EDMAE: An Efficient Decoupled Masked Autoencoder for Standard View Identification in Pediatric Echocardiography
Shanghai Children’s Medical Center, School of Medicine, Shanghai Jiao Tong University（上海交通大学医学院附属上海儿童医学中心）；Shanghai Engineering Research Center of Intelligence Pediatrics (SERCIP)（上海智能儿科工程技术研究中心）；East China Normal University（华东师范大学）；University of Shanghai for Science and Technology（上海理工大学）
Univ Lyon（里昂大学）；INSA-Lyon（里昂国立应用科学学院）；Université Claude Bernard Lyon 1（里昂第一大学克洛德·贝尔纳大学）；UJM-Saint Etienne（圣艾蒂安大学）；CNRS（法国国家科学研究中心）；Inserm（法国国家健康与医学研究院）；Norwegian University of Science and Technology（挪威科技大学）；Centre Hospitalier de Saint-Etienne（圣艾蒂安中心医院）；St. Olavs Hospital（圣奥拉夫医院）；University of Sherbrooke（舍布鲁克大学）
Segmentation of cardiac structures is one of the fundamental steps to estimate volumetric indices of the heart. This step is still performed semi-automatically in clinical routine, and is thus prone to inter- and intra-observer variability. Recent studies have shown that deep learning has the potential to perform fully automatic segmentation. However, the current best solutions still suffer from a lack of robustness. In this work, we introduce an end-to-end multi-task network designed to improve the overall accuracy of cardiac segmentation while enhancing the estimation of clinical indices and…
Echocardiography (echo) is an indispensable tool in a cardiologist's diagnostic armamentarium. To date, almost all echocardiographic parameters require time-consuming manual labeling and measurements by an experienced echocardiographer and exhibit significant variability, owing to the noisy and artifact-laden nature of echo images. For example, mitral inflow (MI) Doppler is used to assess left ventricular (LV) diastolic function, which is of paramount clinical importance to distinguish between different cardiac diseases. In the current work we present a fully automated workflow which leverages…
Atrial Septal Defect Detection in Children Based on Ultrasound Video Using Multiple Instances Learning
Shanghai Children’s Medical Center, School of Medicine, Shanghai Jiaotong University（上海交通大学医学院附属上海儿童医学中心）；Minhang District Centers for Disease Control and Prevention（闵行区疾病预防控制中心）；Xi’an Jiaotong-Liverpool University（西交利物浦大学）；East China Normal University（华东师范大学）；University of Shanghai for Science and Technology（上海理工大学）
Focus, Align, and Diffuse: Time-Series-Aware Keyframe Diffusion for Cardiac Dynamic Synthesis from Sparse Observations
School of Engineering, University of Birmingham（伯明翰大学工程学院）；William Harvey Research Institute, Queen Mary University London（伦敦大学玛丽女王学院威廉·哈维研究所）；Division of Cardiology, Johns Hopkins University School of Medicine（约翰斯·霍普金斯大学医学院心脏病学系）
GE Vingmed Ultrasound, GE Healthcare（通用电气医疗集团GE Vingmed超声部门）；University of Oslo（奥斯陆大学）；Norwegian Computing Center（挪威计算中心）
Two-dimensional echocardiography (2DE) measurements of left ventricle (LV) dimensions are highly significant markers of several cardiovascular diseases. These measurements are often used in clinical care despite suffering from large variability between observers. This variability is due to the challenging nature of accurately finding the correct temporal and spatial location of measurement endpoints in ultrasound images. These images often contain fuzzy boundaries and varying reflection patterns between frames. In this work, we present a convolutional neural network (CNN) based approach to aut…
ETH Zürich（苏黎世联邦理工学院）；University Children’s Hospital Regensburg (KUNO)（雷根斯堡大学儿童医院（KUNO））
Pulmonary hypertension (PH) in newborns and infants is a complex condition associated with several pulmonary, cardiac, and systemic diseases contributing to morbidity and mortality. Therefore, accurate and early detection of PH is crucial for successful management. Using echocardiography, the primary diagnostic tool in pediatrics, human assessment is both time-consuming and expertise-demanding, raising the need for an automated approach. In this work, we present an interpretable multi-view video-based deep learning approach to predict PH for a cohort of 194 newborns using echocardiograms. We u…
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）
Universitat Pompeu Fabra（庞培法布拉大学）；Normandie Univ（诺曼底大学）；UNICAEN（卡昂大学）；CHU de Caen Normandie（卡昂诺曼底大学医院）；Inserm Comete（法国国家健康与医学研究院Comete实验室）；GIP Cyceron（Cyceron集团利益公共机构）；Philips Research（飞利浦研究院）；ICREA（加泰罗尼亚高等研究院）；IDIBAPS（巴塞罗那生物医学研究所）；KULeuven（鲁汶大学）
3D echocardiography is an increasingly popular tool for assessing cardiac remodelling in the right ventricle (RV). It allows quantification of the cardiac chambers without any geometric assumptions, which is the main weakness of 2D echocardiography. However, regional quantification of geometry and function is limited by the lower spatial and temporal resolution and the scarcity of identifiable anatomical landmarks. We developed a technique for regionally assessing the 3 relevant RV regions: apical, inlet and outflow. The method's inputs are end-diastolic (ED) and end-systolic (ES) segmented 3D…
UltraTwin: Towards Cardiac Anatomical Twin Generation from Multi-view 2D Ultrasound
National-Regional Key Technology Engineering Laboratory for Medical Ultrasound（医学超声关键技术研发实验室）；School of Biomedical Engineering, Medical School, Shenzhen University（生物医学工程学院）；Faculty of Applied Sciences, Macao Polytechnic University（应用科学学院）；Centre for CIMIM, Manchester University（CIMIM研究中心）；Center for Cardiovascular Disease（心血管疾病中心）；The Affiliated Suzhou Hospital of Nanjing Medical University（南京医科大学附属苏州医院）；Suzhou Municipal Hospital, Gusu School, Suzhou, China（苏州 municipal 医院）；The First Affiliated Hospital of Kunming Medical University（昆明医科大学第一附属医院）；The First Affiliated Hospital of Anhui Medical University North（安徽医科大学北院）；The Second People’s Hospital（第二人民医院）；The First Affiliated Hospital of Anhui Medical University（安徽医科大学第一附属医院）；The People's Hospital of Tongren City（铜仁市人民医院）；The First People's Hospital of Taicang City（太仓市第一人民医院）；Guangdong Provincial People’s Hospital（广东省人民医院）
Advanced XR-Based 6-DOF Catheter Tracking System for Immersive Cardiac Intervention Training
Dalio Institute of Cardiovascular Imaging（达利奥心血管成像研究所）；Weill Cornell Medicine（威尔康奈尔医学院）；Englander Institute for Precision Medicine（英格兰德精准医学研究所）；Institute for Computational Biomedicine（计算生物医学研究所）
Enhanced Real-Time 6-DOF Extended Reality Catheter Tracking for Evaluating Potential Improvement in Efficiency, Precision, and Depth Perception for Cardiac Interventions
Deep learning networks are being developed in every stage of the MRI workflow and have provided state-of-the-art results. However, this has come at the cost of increased computation requirement and storage. Hence, replacing the networks with compact models at various stages in the MRI workflow can significantly reduce the required storage space and provide considerable speedup. In computer vision, knowledge distillation is a commonly used method for model compression. In our work, we propose a knowledge distillation (KD) framework for the image to image problems in the MRI workflow in order to…
