Automated Characterization of Stenosis in Invasive Coronary Angiography Images with Convolutional Neural Networks
Yale Cardiovascular Research Group（耶鲁心血管研究组）
Benjamin Au, Uri Shaham, Sanket Dhruva, Georgios Bouras, Ecaterina Cristea, Alexandra Lansky MD, Andreas Coppi, Fred Warner, Shu-Xia Li, Harlan Krumholz
The determination of a coronary stenosis and its severity in current clinical workflow is typically accomplished manually via physician visual assessment (PVA) during invasive coronary angiography. While PVA has shown large inter-rater variability, the more reliable and accurate alternative of Quantitative Coronary Angiography (QCA) is challenging to perform in real-time due to the busy workflow in cardiac catheterization laboratories. We propose a deep learning approach based on Convolutional Neural Networks (CNN) that automatically characterizes and analyzes coronary stenoses in real-time by automating clinical tasks performed during QCA. Our deep learning methods for localization, segmentation and classification of stenosis in still-frame invasive coronary angiography (ICA) images of the right coronary artery (RCA) achieve performance of 72.7% localization accuracy, 0.704 dice coefficient and 0.825 C-statistic in each respective task. Integrated in an end-to-end approach, our model's performance shows statistically significant improvement in false discovery rate over the current standard in real-time clinical stenosis assessment, PVA. To the best of the authors' knowledge, this is the first time an automated machine learning system has been developed that can implement tasks performed in QCA, and the first time an automated machine learning system has demonstrated significant improvement over the current clinical standard for rapid RCA stenosis analysis.
Image Sciences Institute, University Medical Center Utrecht（乌得勒支大学医学中心影像科学研究所）；University Medical Center Utrecht（乌得勒支大学医学中心）；Utrecht University（乌得勒支大学）
Various types of atherosclerotic plaque and varying grades of stenosis could lead to different management of patients with coronary artery disease. Therefore, it is crucial to detect and classify the type of coronary artery plaque, as well as to detect and determine the degree of coronary artery stenosis. This study includes retrospectively collected clinically obtained coronary CT angiography (CCTA) scans of 163 patients. To perform automatic analysis for coronary artery plaque and stenosis classification, a multi-task recurrent convolutional neural network is applied on multi-planar reformat…
University Medical Center Utrecht（乌得勒支大学医学中心）；Utrecht University（乌得勒支大学）
In patients with coronary artery stenoses of intermediate severity, the functional significance needs to be determined. Fractional flow reserve (FFR) measurement, performed during invasive coronary angiography (ICA), is most often used in clinical practice. To reduce the number of ICA procedures, we present a method for automatic identification of patients with functionally significant coronary artery stenoses, employing deep learning analysis of the left ventricle (LV) myocardium in rest coronary CT angiography (CCTA). The study includes consecutively acquired CCTA scans of 166 patients with …
Myocardial Region-guided Feature Aggregation Net for Automatic Coronary artery Segmentation and Stenosis Assessment using Coronary Computed Tomography Angiography
School of Computer Science and Technology, Zhengzhou University of Light Industry（郑州轻工业大学计算机与通信工程学院）；School of Information Management and Engineering, Shanghai University of Finance and Economics（上海财经大学信息管理与工程学院）；Department of Applied Computing, Michigan Technological University（密歇根理工大学应用计算系）；Health Research Institute, Michigan Technological University（密歇根理工大学健康研究所）；Department of Computer Science, Kennesaw State University（肯尼索州立大学计算机科学系）
MAC-XA: Multi-view Anatomy-Correspondence Fusion for Coronary Stenosis Reporting from X-ray Angiography
Technical University of Munich（慕尼黑工业大学）；German Heart Center Munich（慕尼黑德国心脏中心）；Munich Center for Machine Learning（慕尼黑机器学习中心）；German Centre for Cardiovascular Research, Munich Heart Alliance（德国心血管研究中心，慕尼黑心脏联盟）；HELIOS Hospital West of Munich（慕尼黑西部黑利奥斯医院）；relAI – Konrad Zuse School of Excellence in Reliable AI（relAI - 康拉德·楚泽可靠人工智能卓越学院）
Image Sciences Institute, University Medical Center Utrecht（乌得勒支大学医学中心影像科学研究所）；University Medical Center Utrecht（乌得勒支大学医学中心）；Utrecht University（乌得勒支大学）；Amsterdam University Medical Center（阿姆斯特丹大学医学中心）
Treatment of patients with obstructive coronary artery disease is guided by the functional significance of a coronary artery stenosis. Fractional flow reserve (FFR), measured during invasive coronary angiography (ICA), is considered the gold standard to define the functional significance of a coronary stenosis. Here, we present a method for non-invasive detection of patients with functionally significant coronary artery stenosis, combining analysis of the coronary artery tree and the left ventricular (LV) myocardium in cardiac CT angiography (CCTA) images. We retrospectively collected CCTA sca…
School of Computer Science and Technology, Anhui University（安徽大学计算机科学与技术学院）；National Heart & Lung Institute, Imperial College London（帝国理工学院国家心肺研究所）；Key Laboratory of Intelligent Computing and Signal Processing, Ministry of Education, Anhui University（安徽大学智能计算与信号处理教育部重点实验室）；Cardiovascular Research Centre, Royal Brompton Hospital（皇家布朗普顿医院心血管研究中心）；School of Biomedical Engineering, Sun Yat-Sen University（中山大学生物医学工程学院）；Western University（韦仕敦大学）
The quantification of the coronary artery stenosis is of significant clinical importance in coronary artery disease diagnosis and intervention treatment. It aims to quantify the morphological indices of the coronary artery lesions such as minimum lumen diameter, reference vessel diameter, lesion length, and these indices are the reference of the interventional stent placement. In this study, we propose a direct multiview quantitative coronary angiography (DMQCA) model as an automatic clinical tool to quantify the coronary artery stenosis from X-ray coronary angiography images. The proposed DMQ…
Fudan University（复旦大学）；Shanghai East Hospital, Tongji University（同济大学附属东方医院）；Ruijin Hospital, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院附属瑞金医院）
We proposed a novel unsupervised methodology named Disarranged Zone Learning (DZL) to automatically recognize stenosis in coronary angiography. The methodology firstly disarranges the frames in a video, secondly it generates an effective zone and lastly trains an encoder-decoder GRU model to learn the capability to recover disarranged frames. The breakthrough of our study is to discover and validate the Sequence Intensity (Recover Difficulty) is a measure of Coronary Artery Stenosis Status. Hence, the prediction accuracy of DZL is used as an approximator of coronary stenosis indicator. DZL is …
Coronary artery disease (CAD) has posed a leading threat to the lives of cardiovascular disease patients worldwide for a long time. Therefore, automated diagnosis of CAD has indispensable significance in clinical medicine. However, the complexity of coronary artery plaques that cause CAD makes the automatic detection of coronary artery stenosis in Coronary CT angiography (CCTA) a difficult task. In this paper, we propose a Transformer network (TR-Net) for the automatic detection of significant stenosis (i.e. luminal narrowing > 50%) while practically completing the computer-assisted diagnosis …
Coronary angiography is the "gold standard" for diagnosing coronary artery disease (CAD). At present, the methods for detecting and evaluating coronary artery stenosis cannot satisfy the clinical needs, e.g., there is no prior study of detecting stenoses in prespecified vessel segments, which is necessary in clinical practice. Two vascular stenosis detection methods are proposed to assist the diagnosis. The first one is an automatic method, which can automatically extract the entire coronary artery tree and mark all the possible stenoses. The second one is an interactive method. With this meth…
To decrease patient waiting time for diagnosis of the Coronary Artery Disease, automatic methods are applied to identify its severity using Coronary Computed Tomography Angiography scans or extracted Multiplanar Reconstruction (MPR) images, giving doctors a second-opinion on the priority of each case. The main disadvantage of previous studies is the lack of large set of data that could guarantee their reliability. Another limitation is the usage of handcrafted features requiring manual preprocessing, such as centerline extraction. We overcome both limitations by applying a different automated …
Image Sciences Institute, University Medical Center Utrecht（乌得勒支大学医学中心影像科学研究所）；University Medical Center Utrecht（乌得勒支大学医学中心）；Amsterdam University Medical Center（阿姆斯特丹大学医学中心）；Utrecht University（乌得勒支大学）
In patients with obstructive coronary artery disease, the functional significance of a coronary artery stenosis needs to be determined to guide treatment. This is typically established through fractional flow reserve (FFR) measurement, performed during invasive coronary angiography (ICA). We present a method for automatic and non-invasive detection of patients requiring ICA, employing deep unsupervised analysis of complete coronary arteries in cardiac CT angiography (CCTA) images. We retrospectively collected CCTA scans of 187 patients, 137 of them underwent invasive FFR measurement in 192 dif…
School of Computer Science and Technology, Ocean University of China（中国海洋大学计算机科学与技术学院）；School of Material Science and Engineering, Ocean University of China（中国海洋大学材料科学与工程学院）；School of Mathematical Sciences, Ocean University of China（中国海洋大学数学科学学院）；Department of Cardiology, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University（山东大学齐鲁医学院齐鲁医院（青岛）心内科）
Coronary angiography is the "gold standard" for diagnosing coronary artery disease (CAD). At present, the methods for detecting and evaluating coronary artery stenosis cannot satisfy the clinical needs, e.g., there is no prior study of detecting stenoses in prespecified vessel segments, which is necessary in clinical practice. Two vascular stenosis detection methods are proposed to assist the diagnosis. The first one is an automatic method, which can automatically extract the entire coronary artery tree and mark all the possible stenoses. The second one is an interactive method. With this meth…
Tsinghua University（清华大学）；Anzhen Hospital, Capital Medical University（首都医科大学附属安贞医院）；Capital Medical University（首都医科大学）；The First Affiliated Hospital of Zhengzhou University（郑州大学第一附属医院）
Cardiac coronary angiography is a major technology to assist doctors during cardiac interventional surgeries. Under the exposure of X-ray radiation, doctors inject contrast agents through catheters to determine the position and status of coronary vessels in real time. To get a coronary angiography video with a high frame rate, the doctor needs to increase the exposure frequency and intensity of the X-ray. This will inevitably increase the X-ray harm to both patients and surgeons. In this work, we innovatively utilize a deep-learning based video interpolation algorithm to interpolate coronary a…
Instituto Superior Técnico（高等技术学院）；University of Lisbon（里斯本大学）；CCUL（里斯本大学中心学院）；Lisbon School of Medicine（里斯本医学院）
Coronary artery disease leading up to stenosis, the partial or total blocking of coronary arteries, is a severe condition that affects millions of patients each year. Automated identification and classification of stenosis severity from minimally invasive procedures would be of great clinical value, but existing methods do not match the accuracy of experienced cardiologists, due to the complexity of the task. Although a number of computational approaches for quantitative assessment of stenosis have been proposed to date, the performance of these methods is still far from the required levels fo…
University Medical Center Utrecht（乌得勒支大学医学中心）；Utrecht University（乌得勒支大学）
Coronary artery centerline extraction in cardiac CT angiography (CCTA) images is a prerequisite for evaluation of stenoses and atherosclerotic plaque. We propose an algorithm that extracts coronary artery centerlines in CCTA using a convolutional neural network (CNN). A 3D dilated CNN is trained to predict the most likely direction and radius of an artery at any given point in a CCTA image based on a local image patch. Starting from a single seed point placed manually or automatically anywhere in a coronary artery, a tracker follows the vessel centerline in two directions using the predictions…
DeepCORO-CLIP: A Multi-View Foundation Model for Comprehensive Coronary Angiography Video-Text Analysis and External Validation
Division of Cardiology, Department of Medicine, Montreal Heart Institute（蒙特利尔心脏研究所心血管科，医学部）；McGill, Faculty of Engineering（麦吉尔大学工程学院）；Division of Cardiology, Department of Medicine, University of California, San Francisco（加州大学旧金山分校医学部心血管科）；Cardiovascular Research Institute, University of California, San Francisco（加州大学旧金山分校心血管研究所）；Bakar Computational Health Sciences Institute, University of California, San Francisco（加州大学旧金山分校Bakar计算健康科学研究所）；Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA（Cedars-Sinai 医疗中心洛杉矶分院心血管科，Smidt心脏研究所）；Department of Bioengineering, University of California Los Angeles（加州大学洛杉矶分校生物工程系）；Department of Medicine, Kaiser Permanente, San Francisco, USA（Kaiser Permanente 旧金山分院医学部）；Department of Medicine, Université de Montréal（蒙特利尔大学医学部）
CORA: Generalizable coronary artery disease assessment and risk stratification from coronary CT angiography using pathology-centric representation learning
Department of Radiology, Northwestern University（西北大学放射学系）；Department of Biomedical Engineering, Northwestern University（西北大学生物医学工程系）；Department of Cardiology, Northwestern University（西北大学心脏病学系）；Department of Preventive Medicine, Northwestern University（西北大学预防医学系）
Michigan Technological University（密歇根理工大学）；University of Southern Mississippi（南密西西比大学）；Tulane University（杜兰大学）；Ochsner Medical Center（奥克斯纳医疗中心）
In stable coronary artery disease (CAD), reduction in mortality and/or myocardial infarction with revascularization over medical therapy has not been reliably achieved. Coronary arteries are usually extracted to perform stenosis detection. We aim to develop an automatic algorithm by deep learning to extract coronary arteries from ICAs.In this study, a multi-input and multi-scale (MIMS) U-Net with a two-stage recurrent training strategy was proposed for the automatic vessel segmentation. Incorporating features such as the Inception residual module with depth-wise separable convolutional layers,…
ConvNeXtv2 Fusion with Mask R-CNN for Automatic Region Based Coronary Artery Stenosis Detection for Disease Diagnosis
Nepal Applied Mathematics and Informatics Institute for research（尼泊尔应用数学与信息学研究所）；Fogsphere(Redev AI Ltd.)（Fogsphere（Redev AI有限公司））；University of Lausanne（洛桑大学）；University of Aberdeen（阿伯丁大学）
The analysis of plaque deposits in the coronary vasculature is an important topic in current clinical research. From a technical side mostly new algorithms for different sub tasks - e.g. centerline extraction or vessel/plaque segmentation - are proposed. However, to enable clinical research with the help of these algorithms, a software solution, which enables manual correction, comprehensive visual feedback and tissue analysis capabilities, is needed. Therefore, we want to present such an integrated software solution. It is able to perform robust automatic centerline extraction and inner and o…
A Deep Learning Model for Coronary Artery Segmentation and Quantitative Stenosis Detection in Angiographic Images
School of Mathematical Sciences, Ocean University of China（中国海洋大学数学科学学院）；School of Haide, Ocean University of China（中国海洋大学海德学院）；School of Ocean and Atmosphere, Ocean University of China（中国海洋大学海洋与大气学院）；Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University（山东大学齐鲁医院（青岛））
SFD-Mamba2Net: Structure-Guided Frequency-Enhanced Dual-Stream Mamba2 Network for Coronary Artery Segmentation
Sichuan Normal University（四川师范大学）；Education Big Data Collaborative Innovation Center of Sichuan 2011（四川省教育大数据协同创新中心）；The First Affiliated Hospital with Nanjing Medical University（南京医科大学第一附属医院）；Michigan Technological University（密歇根理工大学）；Kennesaw State University（肯尼索州立大学）
Image Sciences Institute（影像科学研究所）；University Medical Center Utrecht（乌得勒支大学医学中心）；Department of Radiology（放射科）
Detection of coronary artery stenosis in coronary CT angiography (CCTA) requires highly personalized surface meshes enclosing the coronary lumen. In this work, we propose to use graph convolutional networks (GCNs) to predict the spatial location of vertices in a tubular surface mesh that segments the coronary artery lumen. Predictions for individual vertex locations are based on local image features as well as on features of neighboring vertices in the mesh graph. The method was trained and evaluated using the publicly available Coronary Artery Stenoses Detection and Quantification Evaluation …
ImageCAS-X: a dataset and benchmark for coronary artery segmentation and centerline extraction in coronary CT angiography
DTU Compute, Technical University of Denmark（丹麦技术大学DTU计算学院）；Cardiovascular Research Unit, Copenhagen University Hospital – Rigshospitalet（哥本哈根大学医院——国立医院心血管研究科）
Multi-View Stenosis Classification Leveraging Transformer-Based Multiple-Instance Learning Using Real-World Clinical Data
Chair for AI in Healthcare and Medicine, Technical University of Munich (TUM) and TUM University Hospital（人工智能在医疗与健康中的chair，慕尼黑技术大学及慕尼黑技术大学医院）；Department of Computing, Imperial College London, UK, and Munich Center for Machine Learning (MCML)（计算系，伦敦帝国学院，英国，以及慕尼黑机器学习中心）；Department of Internal Medicine, TUM University Hospital（内科系，慕尼黑技术大学医院）
Coronary Heart Disease (CHD) is a leading cause of death in the modern world. The development of modern analytical tools for diagnostics and treatment of CHD is receiving substantial attention from the scientific community. Deep learning-based algorithms, such as segmentation networks and detectors, play an important role in assisting medical professionals by providing timely analysis of a patient's angiograms. This paper focuses on X-Ray Coronary Angiography (XCA), which is considered to be a "gold standard" in the diagnosis and treatment of CHD. First, we describe publicly available datasets…
Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局生物信息学研究所）；Hwa Chong Institution (College)（华侨中学（高中部））；National Heart Centre Singapore（新加坡国家心脏中心）；Duke-NUS Medical School（杜克-新加坡国立大学医学院）；School of Computing, National University of Singapore（新加坡国立大学计算学院）；Singapore Eye Research Institute (SERI)（新加坡眼科研究所）；Rehabilitation Research Institute of Singapore（新加坡康复研究所）；Institute of Bioengineering and Nanotechnology, A*STAR（新加坡科技研究局生物工程与纳米技术研究所）
The evaluation of obstructions (stenosis) in coronary arteries is currently done by a physician's visual assessment of coronary angiography video sequences. It is laborious, and can be susceptible to interobserver variation. Prior studies have attempted to automate this process, but few have demonstrated an integrated suite of algorithms for the end-to-end analysis of angiograms. We report an automated analysis pipeline based on deep learning to rapidly and objectively assess coronary angiograms, highlight coronary vessels of interest, and quantify potential stenosis. We propose a 3-stage auto…
Deep Learning-Based Automated Quantification of TIMI Myocardial Perfusion Frame Count (DL-TMPFC) from Coronary Angiography: A Novel Framework for Rapid Assessment of Microvascular Dysfunction
School of Artificial Intelligence and Digital Economy Industry, Guangzhou Institute of Science and Technology（人工智能与数字经济发展学院，广州科学研究院）；Department of Cardiology, Second Affiliated Hospital, Jiangxi Medical College, Nanchang University（南华大学江西医学院心内科，第二附属医院）；Department of Cardiology, First Affiliated Hospital of Zhejiang University School of Medicine（浙江大学医学院附属第一医院心内科）；Department of Cardiology, National University Heart Centre（国立大学心脏中心心内科）
CADICA: a new dataset for coronary artery disease detection by using invasive coronary angiography
University of Málaga（马拉加大学）；Instituto de Investigación Biomédica de Málaga y Plataforma en Nanomedicina-IBIMA Plataforma BIONAND（马拉加生物医学与纳米医学平台研究所-IBIMA BIONAND平台）；Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares (CIBERCV)（心血管疾病网络生物医学研究中心）；Instituto de Salud Carlos III (ISCIII)（卡洛斯三世卫生研究所）
Tsinghua University（清华大学）；Beijing Anzhen Hospital, Capital Medical University（首都医科大学附属北京安贞医院）；The First Affiliated Hospital of Zhengzhou University（郑州大学第一附属医院）
Coronary angiography is an indispensable assistive technique for cardiac interventional surgery. Segmentation and extraction of blood vessels from coronary angiography videos are very essential prerequisites for physicians to locate, assess and diagnose the plaques and stenosis in blood vessels. This article proposes a new video segmentation framework that can extract the clearest and most comprehensive coronary angiography images from a video sequence, thereby helping physicians to better observe the condition of blood vessels. This framework combines a 3D convolutional layer to extract spati…
Coronary heart disease (CHD) is the leading cause of adult death in the United States and worldwide, and for which the coronary angiography procedure is the primary gateway for diagnosis and clinical management decisions. The standard-of-care for interpretation of coronary angiograms depends upon ad-hoc visual assessment by the physician operator. However, ad-hoc visual interpretation of angiograms is poorly reproducible, highly variable and bias prone. Here we show for the first time that fully-automated angiogram interpretation to estimate coronary artery stenosis is possible using a sequenc…
Shanghai Jiao Tong University（上海交通大学）；Shanghai Jiao Tong University School of Medicine（上海交通大学医学院）；Ruijin Hospital, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院附属瑞金医院）；Research Institute of Pancreatic Diseases, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院胰腺疾病研究所）
The extraction of contrast-filled vessels from X-ray coronary angiography (XCA) image sequence has important clinical significance for intuitively diagnosis and therapy. In this study, the XCA image sequence is regarded as a 3D tensor input, the vessel layer is regarded as a sparse tensor, and the background layer is regarded as a low-rank tensor. Using tensor nuclear norm (TNN) minimization, a novel method for vessel layer extraction based on tensor robust principal component analysis (TRPCA) is proposed. Furthermore, considering the irregular movement of vessels and the low-frequency dynamic…
Gulbarga University（古尔伯加大学）；S. V. College（S. V.学院）
At present, coronary angiography is the well known standard for the diagnosis of coronary artery disease. Conventional coronary angiography is an invasive procedure with a small, yet inherent risk of myocardial infarction, stroke, potential arrhythmias, and death. Other noninvasive diagnostic tools, such as electrocardiography, echocardiography, and nuclear imaging are now widely available but are limited by their inability to directly visualize and quantify coronary artery stenoses and predict the stability of plaques. Coronary magnetic resonance angiography (MRA) is a technique that allows v…
The University of Georgia（佐治亚大学）；Piedmont Heart Institute（皮德蒙特心脏研究所）；Stony Brook University Medical Center（石溪大学医学中心）
The limitations of conventional imaging techniques have hitherto precluded a thorough and formal investigation of the complex morphology of the left ventricular (LV) endocardial surface and its relation to the severity of Coronary Artery Disease (CAD). Recent developments in high-resolution Multirow-Detector Computed Tomography (MDCT) scanner technology have enabled the imaging of LV endocardial surface morphology in a single heart beat. Analysis of high-resolution Computed Tomography (CT) images from a 320-MDCT scanner allows the study of the relationship between percent Diameter Stenosis (DS…
Queen Mary University of London（伦敦玛丽女王大学）；Barts Health NHS Trust（巴特国民保健信托基金会）
X-ray coronary angiography (XCA) is used to assess coronary artery disease and provides valuable information on lesion morphology and severity. However, XCA images are 2D and therefore limit visualisation of the vessel. 3D reconstruction of coronary vessels is possible using multiple views, however lumen border detection in current software is performed manually resulting in limited reproducibility and slow processing time. In this study we propose 3DAngioNet, a novel deep learning (DL) system that enables rapid 3D vessel mesh reconstruction using 2D XCA images from two views. Our approach lea…
DeepCA: Deep Learning-based 3D Coronary Artery Tree Reconstruction from Two 2D Non-simultaneous X-ray Angiography Projections
University of Oxford（牛津大学）；Institute of Biomedical Engineering（生物医学工程研究所）；Division of Cardiovascular Medicine（心血管医学部）；Radcliffe Department of Medicine（拉德克利夫医学系）；Oxford Acute Vascular Imaging Centre（牛津急性血管成像中心）
Multivessel Coronary Artery Segmentation and Stenosis Localisation using Ensemble Learning
Birmingham City University（伯明翰城市大学）；University of the West of England (UWE)（西英格兰大学）；Institute of Accounting and Administration, University of Aveiro（阿威罗大学会计与行政学院）；NUS High School of Math and Science（新加坡国立大学数学与科学高中）；Qatar University（卡塔尔大学）；Information Technology University（信息技术大学）；Bristol Heart Institute, University of Bristol（布里斯托大学布里斯托心脏研究所）
University of Science and Technology of China（中国科学技术大学）；The First Affiliated Hospital of Dalian Medical University（大连医科大学附属第一医院）
Coronary CT Angiography (CCTA) is susceptible to various distortions (e.g., artifacts and noise), which severely compromise the exact diagnosis of cardiovascular diseases. The appropriate CCTA Vessel-level Image Quality Assessment (CCTA VIQA) algorithm can be used to reduce the risk of error diagnosis. The primary challenges of CCTA VIQA are that the local part of coronary that determines final quality is hard to locate. To tackle the challenge, we formulate CCTA VIQA as a multiple-instance learning (MIL) problem, and exploit Transformer-based MIL backbone (termed as T-MIL) to aggregate the mu…
NeRF-CA: Dynamic Reconstruction of X-ray Coronary Angiography with Extremely Sparse-views
Department of Mathematics & Computer Science, Eindhoven University of Technology（数学与计算机科学系，埃因霍温理工大学）；Philips Healthcare（飞利普医疗）；Department of Electrical Engineering, Eindhoven University of Technology（电子工程系，埃因霍温理工大学）；ASML（ASML公司）
