Personalized 3D Myocardial Infarct Geometry Reconstruction from Cine MRI with Explicit Cardiac Motion Modeling
Department of Biomedical Engineering, National University of Singapore, Singapore（新加坡国立大学生物医学工程系）；Department of Computer Science, National University of Singapore, Singapore（新加坡国立大学计算机科学系）；Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand（奥克兰大学生物工程研究所）；School of Biomedical Engineering and Imaging Sciences, King’s College London, UK（伦敦国王学院生物医学工程与成像科学学校）；Department of Medicine, National University of Singapore, Singapore（新加坡国立大学医学系）；Department of Cardiology, National University Heart Centre Singapore, Singapore（新加坡国立心脏中心心血管科）
Yilin Lyu, Fan Yang, Xiaoyue Liu, Zichen Jiang, Joshua Dillon, Debbie Zhao, Martyn Nash, Charlene Mauger, Alistair Young, Ching-Hui Sia, Mark YY Chan, Lei Li
Accurate representation of myocardial infarct geometry is crucial for patient-specific cardiac modeling in MI patients. While Late gadolinium enhancement (LGE) MRI is the clinical gold standard for infarct detection, it requires contrast agents, introducing side effects and patient discomfort. Moreover, infarct reconstruction from LGE often relies on sparsely sampled 2D slices, limiting spatial resolution and accuracy. In this work, we propose a novel framework for automatically reconstructing high-fidelity 3D myocardial infarct geometry from 2D clinically standard cine MRI, eliminating the need for contrast agents. Specifically, we first reconstruct the 4D biventricular mesh from multi-view cine MRIs via an automatic deep shape fitting model, biv-me. Then, we design a infarction reconstruction model, CMotion2Infarct-Net, to explicitly utilize the motion patterns within this dynamic geometry to localize infarct regions. Evaluated on 205 cine MRI scans from 126 MI patients, our method shows reasonable agreement with manual delineation. This study demonstrates the feasibility of contrast-free, cardiac motion-driven 3D infarct reconstruction, paving the way for efficient digital twin of MI.
Automatic segmentation of myocardial contours and relevant areas like infraction and no-reflow is an important step for the quantitative evaluation of myocardial infarction. In this work, we propose a cascaded convolutional neural network for automatic myocardial infarction segmentation from delayed-enhancement cardiac MRI. We first use a 2D U-Net to focus on the intra-slice information to perform a preliminary segmentation. After that, we use a 3D U-Net to utilize the volumetric spatial information for a subtle segmentation. Our method is evaluated on the MICCAI 2020 EMIDEC challenge dataset …
ImViA Laboratory, University of Burgundy（勃艮第大学ImViA实验室）；CHU Dijon（第戎大学医院）；CGFL；Institut Curie（居里研究所）
In this paper, we propose a new deep learning framework for an automatic myocardial infarction evaluation from clinical information and delayed enhancement-MRI (DE-MRI). The proposed framework addresses two tasks. The first task is automatic detection of myocardial contours, the infarcted area, the no-reflow area, and the left ventricular cavity from a short-axis DE-MRI series. It employs two segmentation neural networks. The first network is used to segment the anatomical structures such as the myocardium and left ventricular cavity. The second network is used to segment the pathological area…
Nanjing University of Science and Technology（南京理工大学）
Automatic evaluation of myocardium and pathology plays an important role in the quantitative analysis of patients suffering from myocardial infarction. In this paper, we present a cascaded convolutional neural network framework for myocardial infarction segmentation and classification in delayed-enhancement cardiac MRI. Specifically, we first use a 2D U-Net to segment the whole heart, including the left ventricle and the myocardium. Then, we crop the whole heart as a region of interest (ROI). Finally, a new 2D U-Net is used to segment the infraction and no-reflow areas in the whole heart ROI. …
Contrast-Free Myocardial Scar Segmentation in Cine MRI using Motion and Texture Fusion
University of Oxford（牛津大学）；University of Southampton（南安普顿大学）；National University of Singapore（新加坡国立大学）；Fudan University（复旦大学）；Zhongshan Hospital, Fudan University（复旦大学附属中山医院）
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…
University of Burgundy（勃艮第大学）；Technical University of Munich（慕尼黑工业大学）；icometrix；Casis company（Casis公司）；University Hospital of Dijon（第戎大学医院）
Significance: Late gadolinium enhanced magnetic resonance imaging (LGE-MRI) is the gold standard technique for myocardial viability assessment. Although the technique accurately reflects the damaged tissue, there is no clinical standard for quantifying myocardial infarction (MI), demanding most algorithms to be expert dependent. Objectives and Methods: In this work a new automatic method for MI quantification from LGE-MRI is proposed. Our novel segmentation approach is devised for accurately detecting not only hyper-enhanced lesions, but also microvascular-obstructed areas. Moreover, it includ…
Personalized 3D Myocardial Infarct Geometry Reconstruction from Cine MRI for Cardiac Digital Twins
Department of Biomedical Engineering, National University of Singapore（新加坡国立大学生物医学工程系）；Department of Medicine, National University of Singapore（新加坡国立大学医学系）；Department of Cardiology, National University Heart Centre Singapore（新加坡国立心脏中心心内科部）
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…
University of Burgundy（勃艮第大学）；University Hospital of Dijon（第戎大学医院）；University of Franche-Comté（弗朗什-孔泰大学）；CASIS Company（CASIS公司）；University of Sousse（苏塞大学）；University of Monastir（莫纳斯提尔大学）；Erasmus MC（伊拉斯姆斯大学医学中心）
A key factor for assessing the state of the heart after myocardial infarction (MI) is to measure whether the myocardium segment is viable after reperfusion or revascularization therapy. Delayed enhancement-MRI or DE-MRI, which is performed several minutes after injection of the contrast agent, provides high contrast between viable and nonviable myocardium and is therefore a method of choice to evaluate the extent of MI. To automatically assess myocardial status, the results of the EMIDEC challenge that focused on this task are presented in this paper. The challenge's main objectives were twofo…
University of Virginia（弗吉尼亚大学）；University of Virginia Health System（弗吉尼亚大学卫生系统）
The selection of an optimal pacing site, which is ideally scar-free and late activated, is critical to the response of cardiac resynchronization therapy (CRT). Despite the success of current approaches formulating the detection of such late mechanical activation (LMA) regions as a problem of activation time regression, their accuracy remains unsatisfactory, particularly in cases where myocardial scar exists. To address this issue, this paper introduces a multi-task deep learning framework that simultaneously estimates LMA amount and classify the scar-free LMA regions based on cine displacement…
University of Bourgogne Franche-Comté（勃艮第-弗朗什孔泰大学）；University Hospital of Dijon（第戎大学医院）；CASIS company（CASIS公司）
Cardiac left ventricular (LV) segmentation from short-axis MRI acquired 10 minutes after the injection of a contrast agent (LGE-MRI) is a necessary step in the processing allowing the identification and diagnosis of cardiac diseases such as myocardial infarction. However, this segmentation is challenging due to high variability across subjects and the potential lack of contrast between structures. Then, the main objective of this work is to develop an accurate automatic segmentation method based on deep learning models for the myocardial borders on LGE-MRI. To this end, 2.5 D residual neural n…
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…
Magnetic Resonance Imaging (MRI) has evolved as a clinical standard-of-care imaging modality for cardiac morphology, function assessment, and guidance of cardiac interventions. All these applications rely on accurate extraction of the myocardial tissue and blood pool from the imaging data. Here we propose a framework for left ventricle (LV) segmentation from cardiac cine-MRI. First, we segment the LV blood pool using iterative graph cuts, and subsequently use this information to segment the myocardium. We formulate the segmentation procedure as an energy minimization problem in a graph subject…
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…
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…
CMRINet: Joint Groupwise Registration and Segmentation for Cardiac Function Quantification from Cine-MRI
Leiden University Medical Center（莱顿大学医学中心）；University of Sheffield（谢菲尔德大学）；INSIGNEO；Institute for In Silico Medicine（计算医学研究所）；Department of Infection, Immunity and Cardiovascular Disease（感染、免疫与心血管疾病部门）；Sheffield Teaching Hospitals（谢菲尔德教学医院）；Department of Clinical Radiology（临床放射学部门）
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）
The MYOSAIQ Challenge: Myocardial Segmentation with Automated Infarct Quantification
INSA-Lyon, Universite Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1294（里昂国立应用科学学院、里昂第一大学克洛德·贝尔纳大学、圣艾蒂安大学、法国国家科学研究中心、法国国家健康与医学研究院、CREATIS联合研究机构）；Institut Universitaire de France (IUF)（法兰西大学研究院）；University of Sherbrooke（谢布鲁克大学）；Huazhong University of Science and Technology（华中科技大学）；Sunnybrook Research Institute, University of Toronto（多伦多大学桑尼布鲁克研究所）；Imperial College London（伦敦帝国理工学院）；University College London（伦敦大学学院）；Fudan University（复旦大学）；Medical University of Graz（格拉茨医科大学）；Graz University of Technology（格拉茨技术大学）；Universidad de Zaragoza（萨拉戈萨大学）；Instituto de Investigación Sanitaria de Aragón (IIS Aragón)（阿拉贡卫生研究所）；Centre Hospitalier Annecy Genevois（阿讷西日内瓦中心医院）；Hôpital Universitaire Cardiologique Louis Pradel（路易·普拉德尔大学心脏病医院）
University of Kansas（堪萨斯大学）；Rochester Institute of Technology（罗切斯特理工学院）
Patient-specific left ventricle (LV) myocardial models have the potential to be used in a variety of clinical scenarios for improved diagnosis and treatment plans. Cine cardiac magnetic resonance (MR) imaging provides high resolution images to reconstruct patient-specific geometric models of the LV myocardium. With the advent of deep learning, accurate segmentation of cardiac chambers from cine cardiac MR images and unsupervised learning for image registration for cardiac motion estimation on a large number of image datasets is attainable. Here, we propose a deep leaning-based framework for th…
National Heart and Lung Institute, Imperial College London（帝国理工学院国家心肺研究所）；Imperial College London（帝国理工学院）；Royal Brompton Hospital（皇家布朗普顿医院）
Temporal patterns of cardiac motion provide important information for cardiac disease diagnosis. This pattern could be obtained by three-directional CINE multi-slice left ventricular myocardial velocity mapping (3Dir MVM), which is a cardiac MR technique providing magnitude and phase information of the myocardial motion simultaneously. However, long acquisition time limits the usage of this technique by causing breathing artifacts, while shortening the time causes low temporal resolution and may provide an inaccurate assessment of cardiac motion. In this study, we proposed a frame synthesis al…
We propose a method to classify cardiac pathology based on a novel approach to extract image derived features to characterize the shape and motion of the heart. An original semi-supervised learning procedure, which makes efficient use of a large amount of non-segmented images and a small amount of images segmented manually by experts, is developed to generate pixel-wise apparent flow between two time points of a 2D+t cine MRI image sequence. Combining the apparent flow maps and cardiac segmentation masks, we obtain a local apparent flow corresponding to the 2D motion of myocardium and ventricu…
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 Leicester（莱斯特大学）；NIHR Leicester Biomedical Research Centre（NIHR莱斯特生物医学研究中心）；Glenfield Hospital（格伦菲尔德医院）；Norwegian University of Science and Technology (NTNU)（挪威科技大学）；Saint-Petersburg State Electrotechnical University（圣彼得堡国立电工大学）
A "heart attack" or myocardial infarction (MI), occurs when an artery supplying blood to the heart is abruptly occluded. The "gold standard" method for imaging MI is Cardiovascular Magnetic Resonance Imaging (MRI), with intravenously administered gadolinium-based contrast (late gadolinium enhancement). However, no "gold standard" fully automated method for the quantification of MI exists. In this work, we propose an end-to-end fully automatic system (MyI-Net) for the detection and quantification of MI in MRI images. This has the potential to reduce the uncertainty due to the technical variabil…
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…
Friedrich-Alexander-University Erlangen-Nuremberg（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）
Quantitative assessment of cardiac left ventricle (LV) morphology is essential to assess cardiac function and improve the diagnosis of different cardiovascular diseases. In current clinical practice, LV quantification depends on the measurement of myocardial shape indices, which is usually achieved by manual contouring of the endo- and epicardial. However, this process subjected to inter and intra-observer variability, and it is a time-consuming and tedious task. In this paper, we propose a spatio-temporal multi-task learning approach to obtain a complete set of measurements quantifying cardia…
Myocardium segmentation of late gadolinium enhancement (LGE) Cardiac MR images is important for evaluation of infarction regions in clinical practice. The pathological myocardium in LGE images presents distinctive brightness and textures compared with the healthy tissues, making it much more challenging to be segment. Instead, the balanced-Steady State Free Precession (bSSFP) cine images show clearly boundaries and can be easily segmented. Given this fact, we propose a novel shape-transfer GAN for LGE images, which can 1) learn to generate realistic LGE images from bSSFP with the anatomical sh…
University of Edinburgh（爱丁堡大学）；Fudan University（复旦大学）；Imperial College London（帝国理工学院）；Technical University of Munich（慕尼黑工业大学）
Myocardial motion and deformation are rich descriptors that characterize cardiac function. Image registration, as the most commonly used technique for myocardial motion tracking, is an ill-posed inverse problem which often requires prior assumptions on the solution space. In contrast to most existing approaches which impose explicit generic regularization such as smoothness, in this work we propose a novel method that can implicitly learn an application-specific biomechanics-informed prior and embed it into a neural network-parameterized transformation model. Particularly, the proposed method …
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 and Lung Institute, Imperial College London（帝国理工学院国家心肺研究所）；Royal Brompton Hospital（皇家布朗普顿医院）；University of the Basque Country UPV/EHU（巴斯克大学）；TECNALIA, Basque Research and Technology Alliance（巴斯克研究与技术联盟TECNALIA）；Beihang University（北京航空航天大学）；Shenzhen Second People’s Hospital（深圳市第二人民医院）
Synthetic digital twins based on medical data accelerate the acquisition, labelling and decision making procedure in digital healthcare. A core part of digital healthcare twins is model-based data synthesis, which permits the generation of realistic medical signals without requiring to cope with the modelling complexity of anatomical and biochemical phenomena producing them in reality. Unfortunately, algorithms for cardiac data synthesis have been so far scarcely studied in the literature. An important imaging modality in the cardiac examination is three-directional CINE multi-slice myocardial…
Accurate cardiac computing, analysis and modeling from multi-modality images are important for the diagnosis and treatment of cardiac disease. Late gadolinium enhancement magnetic resonance imaging (LGE MRI) is a promising technique to visualize and quantify myocardial infarction (MI) and atrial scars. Automating quantification of MI and atrial scars can be challenging due to the low image quality and complex enhancement patterns of LGE MRI. Moreover, compared with the other sequences LGE MRIs with gold standard labels are particularly limited, which represents another obstacle for developing …
Universitat Pompeu Fabra（庞培法布拉大学）；BCN MedTech（巴塞罗那医疗科技中心）；Queen Mary University of London（伦敦玛丽女王大学）；William Harvey Research Institute（威廉·哈维研究所）；Stanford University（斯坦福大学）；Catalan Institution for Research and Advanced Studies (ICREA)（加泰罗尼亚研究与高级研究所）
Use expert visualization or conventional clinical indices can lack accuracy for borderline classications. Advanced statistical approaches based on eigen-decomposition have been mostly concerned with shape and motion indices. In this paper, we present a new approach to identify CVDs from cine-MRI by estimating large pools of radiomic features (statistical, shape and textural features) encoding relevant changes in anatomical and image characteristics due to CVDs. The calculated cine-MRI radiomic features are assessed using sequential forward feature selection to identify the most relevant ones f…
In the clinical environment, myocardial infarction (MI) as one com-mon cardiovascular disease is mainly evaluated based on the late gadolinium enhancement (LGE) cardiac magnetic resonance images (CMRIs). The auto-matic segmentations of left ventricle (LV), right ventricle (RV), and left ven-tricular myocardium (LVM) in the LGE CMRIs are desired for the aided diag-nosis in clinic. To accomplish this segmentation task, this paper proposes a modified U-net architecture by combining multi-sequence CMRIs, including the cine, LGE, and T2-weighted CMRIs. The cine and T2-weighted CMRIs are used to ass…
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）
