Automated construction of surface geometries of cardiac structures from volumetric medical images is important for a number of clinical applications. While deep-learning-based approaches have demonstrated promising reconstruction precision, these approaches have mostly focused on voxel-wise segmentation followed by surface reconstruction and post-processing techniques. However, such approaches suffer from a number of limitations including disconnected regions or incorrect surface topology due to erroneous segmentation and stair-case artifacts due to limited segmentation resolution. We propose a novel deep-learning-based approach that directly predicts whole heart surface meshes from volumetric CT and MR image data. Our approach leverages a graph convolutional neural network to predict deformation on mesh vertices from a pre-defined mesh template to reconstruct multiple anatomical structures in a 3D image volume. Our method demonstrated promising performance of generating whole heart reconstructions with as good or better accuracy than prior deep-learning-based methods on both CT and MR data. Furthermore, by deforming a template mesh, our method can generate whole heart geometries with better anatomical consistency and produce high-resolution geometries from lower resolution input image data. Our method was also able to produce temporally consistent surface mesh predictions for heart motion from CT or MR cine sequences, and therefore can potentially be applied for efficiently constructing 4D whole heart dynamics. Our code and pre-trained networks are available at https://github.com/fkong7/MeshDeformNet
Accurate and robust whole heart substructure segmentation is crucial in developing clinical applications, such as computer-aided diagnosis and computer-aided surgery. However, segmentation of different heart substructures is challenging because of inadequate edge or boundary information, the complexity of the background and texture, and the diversity in different substructures' sizes and shapes. This article proposes a framework for multi-class whole heart segmentation employing non-rigid registration-based probabilistic atlas incorporating the Bayesian framework. We also propose a non-rigid r…
Personalized 4D Whole Heart Geometry Reconstruction from Cine MRI for Cardiac Digital Twins
Department of Biomedical Engineering, National University of Singapore, Singapore（新加坡国立大学生物医学工程系）；School of Data Science, Fudan University, Shanghai, China（复旦大学数据科学学院）；Department of Engineering Science, University of Oxford, Oxford, UK（牛津大学工程科学系）；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内科部）
University of Notre Dame（圣母大学）；Guangdong General Hospital（广东省人民医院）
3D printing has been widely adopted for clinical decision making and interventional planning of Congenital heart disease (CHD), while whole heart and great vessel segmentation is the most significant but time-consuming step in the model generation for 3D printing. While various automatic whole heart and great vessel segmentation frameworks have been developed in the literature, they are ineffective when applied to medical images in CHD, which have significant variations in heart structure and great vessel connections. To address the challenge, we leverage the power of deep learning in processi…
Personalized 4D Whole-Heart Mesh Reconstruction from Cine MRI via Multi-Scale Temporal Modeling and Differentiable Contour Rendering
Department of Biomedical Engineering, National University of Singapore（新加坡国立大学生物医学工程系）；School of Automation, Southeast University（东南大学自动化学院）；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（新加坡国立大学心脏中心心脏病学系）
End-to-End 4D Heart Mesh Recovery Across Full-Stack and Sparse Cardiac MRI
School of Computer and Communication Science, EPFL（计算机与通信科学学院，瑞士联邦理工学院）；ELLIS Institute Finland, Aalto University, Finland（芬兰ELLIS研究所，阿尔托大学，芬兰）；University of North Carolina at Charlotte（北卡罗来纳大学夏洛特分校）；Center for Interventional MRI, CHUV（介入MRI中心，CHUV）
Imperial College London（帝国理工学院）；National University of Defense Technology（国防科技大学）；MRC London Institute of Medical Sciences（英国医学研究委员会伦敦医学科学研究所）；Technical University Munich（慕尼黑工业大学）
3D motion estimation from cine cardiac magnetic resonance (CMR) images is important for the assessment of cardiac function and diagnosis of cardiovascular diseases. Most of the previous methods focus on estimating pixel-/voxel-wise motion fields in the full image space, which ignore the fact that motion estimation is mainly relevant and useful within the object of interest, e.g., the heart. In this work, we model the heart as a 3D geometric mesh and propose a novel deep learning-based method that can estimate 3D motion of the heart mesh from 2D short- and long-axis CMR images. By developing a …
CineMesh4D: Personalized 4D Whole Heart Reconstruction from Sparse Cine MRI
Department of Biomedical Engineering, National University of Singapore, Singapore（新加坡国立大学生物医学工程系）；School of Automation, Southeast University, Nanjing, China（东南大学自动化学院）；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内科部）
本文提出 CineMesh4D，一种端到端的 4D 全心脏重建方法，通过跨域映射从多视角 2D cine MRI 直接重建患者特定的全心脏网格，提升重建质量和运动一致性。
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. …
From Raw Segmentations to Simulation-Ready Cardiac Meshes: An Automated Framework for Anatomical Reconstruction and Virtual Cohort Generation
Gran Sasso Science Institute (GSSI)（大萨索科学研究所）；European Hospital（欧洲医院）；University of Edinburgh（爱丁堡大学）；Physics of Fluids Group, University of Twente（特温特大学流体物理小组）；INFN–Laboratori Nazionali del Gran Sasso（意大利国家核物理研究所大萨索国家实验室）
Shape of my heart: Cardiac models through learned signed distance functions
Institute for Applied Mathematics, University of Bonn（波恩大学应用数学研究所）；University of Graz（格拉茨大学）；Institute for Biological and Medical Engineering, Pontificia Universidad Católica de Chile（智利天主教大学生物与医学工程研究所）；Center for Computational Medicine in Cardiology, Università della Svizzera italiana（瑞士意大利语区大学心血管计算医学中心）；FernUni Schweiz（瑞士远程教育大学）；Instituto Cardiocentro Ticino, EOC（提契诺心脏中心研究所（EOC））；University of Trento（特伦托大学）
Towards Cardiac MRI Foundation Models: Comprehensive Visual-Tabular Representations for Whole-Heart Assessment and Beyond
School of Computation, Information and Technology, Technical University of Munich（慕尼黑工业大学计算、信息与技术学院）；School of Medicine, Klinikum Rechts der Isar, Technical University of Munich（慕尼黑工业大学伊萨尔河畔右岸医院医学院）；Data Science Institute, Imperial College London（帝国理工学院数据科学研究所）；Department of Computing, Imperial College London（帝国理工学院计算系）；Department of Computer Science, University of Sheffield（谢菲尔德大学计算机科学系）；Department of Quantitative Biomedicine, University of Zurich（苏黎世大学定量生物医学系）
State-of-the-Art Stroke Lesion Segmentation at 1/1000th of Parameters
Center for Data Science, Nell Hodgson Woodruff School of Nursing（内尔·霍奇森·伍德拉夫护理学院数据科学中心）；Emory University（埃默里大学）；University of South Carolina（南卡罗来纳大学）；Georgia State University（佐治亚州立大学）
DeepMesh: Mesh-based Cardiac Motion Tracking using Deep Learning
Imperial College London（帝国理工学院）；University of Birmingham（伯明翰大学）；MRC London Institute of Medical Sciences（英国医学研究理事会伦敦医学科学研究所）；Technical University Munich（慕尼黑工业大学）；Klinikum rechts der Isar（伊萨尔河畔临床教学中心）
本文提出 DeepMesh 学习框架，利用多视图二维 CMR 图像重建个体心脏三维网格并估计逐顶点运动场，在 UK Biobank 上优于现有图像与网格类方法。
Image-based computer simulation of cardiac function can be used to probe the mechanisms of (patho)physiology, and guide diagnosis and personalized treatment of cardiac diseases. This paradigm requires constructing simulation-ready meshes of cardiac structures from medical image data--a process that has traditionally required significant time and human effort, limiting large-cohort analyses and potential clinical translations. We propose a novel deep learning approach to reconstruct simulation-ready whole heart meshes from volumetric image data. Our approach learns to deform a template mesh to …
Purdue University（普渡大学）；Carmel High School（卡梅尔高中）；Case Western Reserve University（凯斯西储大学）；The Pennsylvania State University（宾夕法尼亚州立大学）；Georgia Institute of Technology（佐治亚理工学院）；University at Albany, State University of New York（纽约州立大学奥尔巴尼分校）
Preserving Cardiac Integrity: A Topology-Infused Approach to Whole Heart Segmentation
Imperial College London（帝国理工学院）；Jilin university（吉林大学）；National Heart and Lung Institute, Imperial College London（帝国理工学院国家心肺研究所）；School of Biomedical Engineering & Imaging Sciences, King’s College London（伦敦国王学院生物医学工程与影像科学学院）；Cardiovascular Research Centre, Royal Brompton Hospital（皇家布朗普顿医院心血管研究中心）
Purpose: To develop and validate an acquisition and processing technique that enables fully self-gated 4D flow imaging with whole-heart coverage in a fixed five-minute scan. Theory and Methods: The data are acquired continuously using Cartesian sampling and sorted into respiratory and cardiac bins using the self-gating signal. The reconstruction is performed using a recently proposed Bayesian method called ReVEAL4D. ReVEAL4D is validated using data from eight healthy volunteers and two patients and compared with a compressed sensing technique, L1-SENSE. Results: Healthy subjects -- Compared to…
School of Computation, Information and Technology（计算信息学院）；Technical University of Munich（慕尼黑技术大学）；School of Medicine, Klinikum Rechts der Isar（医学院）；Department of Computer Science（计算机科学系）；University of Sheffield（谢菲尔德大学）；Department of Quantitative Biomedicine（定量生物医学系）；University of Zurich（苏黎世大学）；Department of Computing（计算系）；Imperial College London（伦敦帝国理工学院）；Department of Engineering Science（工程科学系）；University of Oxford（牛津大学）
University College London（伦敦大学学院）；Great Ormond Street Hospital（大奥蒙德街医院）；University and ETH Zurich（苏黎世大学与苏黎世联邦理工学院）；University of Oulu（奥卢大学）
Background: Three-dimensional, whole heart, balanced steady state free precession (WH-bSSFP) sequences provide delineation of intra-cardiac and vascular anatomy. However, they have long acquisition times. Here, we propose significant speed ups using a deep learning single volume super resolution reconstruction, to recover high resolution features from rapidly acquired low resolution WH-bSSFP images. Methods: A 3D residual U-Net was trained using synthetic data, created from a library of high-resolution WH-bSSFP images by simulating 0.5 slice resolution and 0.5 phase resolution. The trained net…
Neural Implicit Heart Coordinates: 3D cardiac shape reconstruction from sparse segmentations
School of Biomedical Engineering and Imaging Sciences（生物医学工程与成像科学学院）；William Harvey Research Institute, NIHR Barts Biomedical Research Centre（威廉·哈里森研究机构、NIHR巴茨生物医学研究中心）；Simula Research Laboratory（Simula研究实验室）；Department of Informatics（信息学院）；Department of Bioengineering（生物工程学院）；Barts Heart Centre, Barts Health NHS Trust（巴茨心脏中心、巴茨健康 NHS信托）；Technical University of Munich（慕尼黑技术大学）
S2MNet: Speckle-To-Mesh Net for Three-Dimensional Cardiac Morphology Reconstruction via Echocardiogram
Department of Statistics, University of Georgia（佐治亚大学统计学系）；Department of Statistics, Havard University（哈佛大学统计学系）；Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital（中国医学科学院老年医学研究所）
Improved 3D Whole Heart Geometry from Sparse CMR Slices
School of Biomedical Engineering and Imaging Sciences, King’s College London（伦敦国王学院生物医学工程与影像科学学院）；College of Mathematical Medicine, Zhejiang Normal University（浙江师范大学数学医学院）；Imperial College London（伦敦帝国学院）；HeartFlow, Inc.（HeartFlow公司）；University/BHF Centre for Cardiovascular Science, University of Edinburgh（爱丁堡大学大学/英国心脏基金会心血管科学中心）
Background: RSN whole-heart CMRA is a technique that estimates and corrects for respiratory motion. However, RSN has been limited to a 1D rigid correction which is often insufficient for patients with complex respiratory patterns. The goal of this work is therefore to improve the robustness and quality of 3D radial CMRA by incorporating both 3D motion information and nonrigid intra-acquisition correction of the data into a framework called focused navigation (fNAV). Methods: We applied fNAV to 500 data sets from a numerical simulation, 22 healthy volunteers, and 549 cardiac patients. We compar…
Coronary computed tomography angiography (CCTA) provides detailed an-atomical information on all chambers of the heart. Existing segmentation tools can label the gross anatomy, but addition of application-specific labels can require detailed and often manual refinement. We developed a U-Net based framework to i) extrapolate a new label from existing labels, and ii) parcellate one label into multiple labels, both using label-to-label mapping, to create a desired segmentation that could then be learnt directly from the image (image- to-label mapping). This approach only required manual correctio…
Lausanne University Hospital and University of Lausanne（洛桑大学医院和洛桑大学）；Siemens Healthcare AG（西门子医疗股份公司）；Center for Biomedical Imaging (CIBM)（生物医学成像中心）
Purpose: Whole-heart MRA techniques typically target pre-determined motion states and address cardiac and respiratory dynamics independently. We propose a novel fast reconstruction algorithm, applicable to ungated free-running sequences, that leverages inherent similarities in the acquired data to avoid such physiological constraints. Theory and Methods: The proposed SIMilarity-Based Angiography (SIMBA) method clusters the continuously acquired k-space data in order to find a motion-consistent subset that can be reconstructed into a motion-suppressed whole-heart MRA. Free-running 3D radial dat…
University of Amsterdam（阿姆斯特丹大学）；University Medical Center Utrecht（乌得勒支大学医学中心）
Deep learning-based whole-heart segmentation in coronary CT angiography (CCTA) allows the extraction of quantitative imaging measures for cardiovascular risk prediction. Automatic extraction of these measures in patients undergoing only non-contrast-enhanced CT (NCCT) scanning would be valuable. In this work, we leverage information provided by a dual-layer detector CT scanner to obtain a reference standard in virtual non-contrast (VNC) CT images mimicking NCCT images, and train a 3D convolutional neural network (CNN) for the segmentation of VNC as well as NCCT images. Contrast-enhanced acquis…
With the recent introduction of CT scanners with large cone angles, wide coverage detectors now provide a desirable scanning platform for cardiac CT that allows whole heart imaging in a single rotation. On these scanners, while half-scan data is strictly sufficient to produce images with the best temporal resolution, acquiring a full 360 degree rotation worth of data is beneficial for wide-cone image reconstruction at negligible additional radiation dose. Applying Model-Based Iterative Reconstruction (MBIR) algorithm to the heart has shown to yield significant enhancement in image quality for …
King’s College London（伦敦国王学院）；Ultromics Ltd.（Ultromics有限公司）
Accurate geometric quantification of the human heart is a key step in the diagnosis of numerous cardiac diseases, and in the management of cardiac patients. Ultrasound imaging is the primary modality for cardiac imaging, however acquisition requires high operator skill, and its interpretation and analysis is difficult due to artifacts. Reconstructing cardiac anatomy in 3D can enable discovery of new biomarkers and make imaging less dependent on operator expertise, however most ultrasound systems only have 2D imaging capabilities. We propose both a simple alteration to the Pix2Vox++ networks fo…
