Cardiac computed tomography (CT) has emerged as a major imaging modality for the diagnosis and monitoring of cardiovascular diseases. High temporal resolution is essential to ensure diagnostic accuracy. Limited-angle data acquisition can reduce scan time and improve temporal resolution, but typically leads to severe image degradation and motivates for improved reconstruction techniques. In this paper, we propose a novel physics-informed score-based diffusion model (PSDM) for limited-angle reconstruction of cardiac CT. At the sampling time, we combine a data prior from a diffusion model and a model prior obtained via an iterative algorithm and Fourier fusion to further enhance the image quality. Specifically, our approach integrates the primal-dual hybrid gradient (PDHG) algorithm with score-based diffusion models, thereby enabling us to reconstruct high-quality cardiac CT images from limited-angle data. The numerical simulations and real data experiments confirm the effectiveness of our proposed approach.
Yale University（耶鲁大学）；Yale School of Medicine（耶鲁医学院）
Learning spatial-temporal correspondences in cardiac motion from images is important for understanding the underlying dynamics of cardiac anatomical structures. Many methods explicitly impose smoothness constraints such as the $\mathcal{L}_2$ norm on the displacement vector field (DVF), while usually ignoring biomechanical feasibility in the transformation. Other geometric constraints either regularize specific regions of interest such as imposing incompressibility on the myocardium or introduce additional steps such as training a separate network-based regularizer on physically simulated data…
Over the past decades, the development of CT technologies has been largely driven by the needs for cardiac imaging but the temporal resolution remains insufficient for clinical CT in difficult cases and rather challenging for preclinical micro-CT since small animals, as human cardiac disease models, have much higher heart rates than human. To address this challenge, here we report a Stationary Multi-source AI-based Real-time Tomography (SMART) micro-CT system. This unique scanner is featured by 29 source-detector pairs fixed on a circular track to collect x-ray signals in parallel, enabling in…
Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, United States Berlin Ultrahigh Field Facility (B.U.F.F.), Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Germany Department of Imaging Physics, Delft University of Technology, Netherlands TUM School of Computation, Information；Technology, Technische Universität München, Germany Department of Surgery, Taipei Veterans General Hospital, Taiwan
Multiparametric mapping MRI has become a viable tool for myocardial tissue characterization. However, misalignment between multiparametric maps makes pixel-wise analysis challenging. To address this challenge, we developed a generalizable physics-informed deep-learning model using test-time adaptation to enable group image registration across contrast weighted images acquired from multiple physical models (e.g., a T1 mapping model and T2 mapping model). The physics-informed adaptation utilized the synthetic images from specific physics model as registration reference, allows for transductive l…
Personalized Topology-Informed Localization of Standard 12-Lead ECG Electrode Placement from Incomplete Cardiac MRIs for Efficient Cardiac Digital Twins
School of Electronics & Computer Science, University of Southampton（南安普顿大学电子与计算机科学学院）；University of Oxford（牛津大学）；National University of Singapore（新加坡国立大学）；School of Biomedical Engineering and Imaging Sciences, Kings College London（伦敦国王学院生物医学工程与影像科学学院）
Pontificia Universidad Católica de Chile（智利天主教大学）；University of Graz（格拉茨大学）；Medical University of Graz（格拉茨医科大学）；University of Pennsylvania（宾夕法尼亚大学）；Università della Svizzera italiana（瑞士意大利语区大学）
We propose FiberNet, a method to estimate \emph{in-vivo} the cardiac fiber architecture of the human atria from multiple catheter recordings of the electrical activation. Cardiac fibers play a central role in the electro-mechanical function of the heart, yet they are difficult to determine in-vivo, and hence rarely truly patient-specific in existing cardiac models. FiberNet learns the fiber arrangement by solving an inverse problem with physics-informed neural networks. The inverse problem amounts to identifying the conduction velocity tensor of a cardiac propagation model from a set of sparse…
WarpPINN-fibers: improved cardiac strain estimation from cine-MR with physics-informed neural networks
Millennium Institute for Intelligent Healthcare Engineering, iHEALTH；Department of Mechanical；Metallurgical Engineering, School of Engineering, Pontificia Universidad Cat\'olica de Chile, Santiago, Chile；Institute for Biological；Medical Engineering, Schools of Engineering, Medicine
Johns Hopkins University School of Medicine（约翰斯·霍普金斯大学医学院）；Johns Hopkins University（约翰斯·霍普金斯大学）；Johns Hopkins Hospital（约翰斯·霍普金斯医院）
Visualizing disease-induced scarring and fibrosis in the heart on cardiac magnetic resonance (CMR) imaging with contrast enhancement (LGE) is paramount in characterizing disease progression and quantifying pathophysiological substrates of arrhythmias. However, segmentation and scar/fibrosis identification from LGE-CMR is an intensive manual process prone to large inter-observer variability. Here, we present a novel fully-automated anatomically-informed deep learning solution for left ventricle (LV) and scar/fibrosis segmentation and clinical feature extraction from LGE-CMR. The technology invo…
Universidade Federal Fluminense (UFF)（弗鲁米嫩塞联邦大学）；Universidade Federal do Rio de Janeiro (UFRJ)（里约热内卢联邦大学）；Universidade Federal do Espírito Santo (UFES)（圣埃斯皮里图联邦大学）
The deposits of fat on the surroundings of the heart are correlated to several health risk factors such as atherosclerosis, carotid stiffness, coronary artery calcification, atrial fibrillation and many others. These deposits vary unrelated to obesity, which reinforces its direct segmentation for further quantification. However, manual segmentation of these fats has not been widely deployed in clinical practice due to the required human workload and consequential high cost of physicians and technicians. In this work, we propose a unified method for an autonomous segmentation and quantification…
Universidad de Valladolid（巴利亚多利德大学）；King’s College London（伦敦国王学院）；Instituto Superior Técnico, Universidade de Lisboa（里斯本大学高等技术学院）；Centre for Marine Sciences - CCMAR（海洋科学中心（CCMAR））
First-pass perfusion cardiac magnetic resonance (FPP-CMR) is becoming an essential non-invasive imaging method for detecting deficits of myocardial blood flow, allowing the assessment of coronary heart disease. Nevertheless, acquisitions suffer from relatively low spatial resolution and limited heart coverage. Compressed sensing (CS) methods have been proposed to accelerate FPP-CMR and achieve higher spatial resolution. However, the long reconstruction times have limited the widespread clinical use of CS in FPP-CMR. Deep learning techniques based on supervised learning have emerged as alternat…
TAI-GAN: A Temporally and Anatomically Informed Generative Adversarial Network for early-to-late frame conversion in dynamic cardiac PET inter-frame motion correction
Yale University（耶鲁大学）；IBM Research（IBM研究院）；Yale New Haven Hospital（耶鲁-纽黑文医院）；Yale School of Medicine（耶鲁医学院）
Automatic Left Ventricular Cavity Segmentation via Deep Spatial Sequential Network in 4D Computed Tomography Studies
School of Biomedical Engineering, Shanghai Jiao Tong University（上海交通大学生物医学工程学院）；School of Computer Science, University of Sydney（悉尼大学计算机学院）；Ruijin Hospital, Shanghai Jiaotong University School of Medicine（上海交通大学医学院附属瑞金医院）
IMC-PINN-FE: A Physics-Informed Neural Network for Patient-Specific Left Ventricular Finite Element Modeling with Image Motion Consistency and Biomechanical Parameter Estimation
Department of Bioengineering, Imperial College London（帝国理工学院生物工程系）
Ensemble learning of the atrial fiber orientation with physics-informed neural networks
Pontificia Universidad Católica de Chile（智利天主教大学）；University of Trento（特伦托大学）；Università della Svizzera italiana（瑞士意大利语大学）；Institute for Biological and Medical Engineering（生物与医学工程研究所）；Millennium Institute for Intelligent Healthcare Engineering, iHEALTH（千禧智能医疗工程研究所iHEALTH）
Transformer-based cardiac substructure segmentation from contrast and non-contrast computed tomography for radiotherapy planning
Memorial Sloan Kettering Cancer Center（纪念斯隆·凯特琳癌症中心）；Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg（弗莱堡大学医学院大学医学中心）；German Cancer Consortium (DKTK)（德国癌症联盟（DKTK））
Imperial College London（伦敦帝国学院）；City, University of London（伦敦城市大学）；University of Birmingham（伯明翰大学）
Deep learning has become the most widely used approach for cardiac image segmentation in recent years. In this paper, we provide a review of over 100 cardiac image segmentation papers using deep learning, which covers common imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound (US) and major anatomical structures of interest (ventricles, atria and vessels). In addition, a summary of publicly available cardiac image datasets and code repositories are included to provide a base for encouraging reproducible research. Finally, we discuss the chall…
WarpPINN: Cine-MR image registration with physics-informed neural networks
University of Bath（巴斯大学）；Pontificia Universidad Católica de Valparaíso（瓦尔帕莱索天主大学）；Millennium Institute for Intelligent Healthcare Engineering（千年智能医疗工程研究所）；Pontificia Universidad Católica de Chile（智利天主大学）
Point cloud-based registration and image fusion between cardiac SPECT MPI and CTA
Xi’an University of Posts and Telecommunications（西安邮电大学）；Nanjing Medical University（南京医科大学）；The First Affiliated Hospital of Nanjing Medical University（南京医科大学第一附属医院）；The First Affiliated Hospital of Xi’an Jiaotong University（西安交通大学第一附属医院）；Kennesaw State University（肯尼索州立大学）；Michigan Technological University（密歇根理工大学）
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 …
Columbia University（哥伦比亚大学）；Stevens Institute of Technology（史蒂文斯理工学院）；Imperial College London（帝国理工学院）；Institut Polytechnique de Paris（巴黎理工学院）
Automatically identifying the structural substrates underlying cardiac abnormalities can potentially provide real-time guidance for interventional procedures. With the knowledge of cardiac tissue substrates, the treatment of complex arrhythmias such as atrial fibrillation and ventricular tachycardia can be further optimized by detecting arrhythmia substrates to target for treatment (i.e., adipose) and identifying critical structures to avoid. Optical coherence tomography (OCT) is a real-time imaging modality that aids in addressing this need. Existing approaches for cardiac image analysis main…
Rensselaer Polytechnic Institute（伦斯勒理工学院）；Cedars-Sinai Medical Center（西达赛奈医疗中心）；University of California, Los Angeles（加州大学洛杉矶分校）
Cine cardiac magnetic resonance imaging (MRI) is widely used for diagnosis of cardiac diseases thanks to its ability to present cardiovascular features in excellent contrast. As compared to computed tomography (CT), MRI, however, requires a long scan time, which inevitably induces motion artifacts and causes patients' discomfort. Thus, there has been a strong clinical motivation to develop techniques to reduce both the scan time and motion artifacts. Given its successful applications in other medical imaging tasks such as MRI super-resolution and CT metal artifact reduction, deep learning is a…
Monte Carlo algorithms have a growing impact on nuclear medicine reconstruction processes. One of the main limitations of myocardial perfusion imaging (MPI) is the effective mitigation of the scattering component, which is particularly challenging in Single Photon Emission Computed Tomography (SPECT). In SPECT, no timing information can be retrieved to locate the primary source photons. Monte Carlo methods allow an event-by-event simulation of the scattering kinematics, which can be incorporated into a model of the imaging system response. This approach was adopted since the late Nineties by s…
Informed Sinogram Interpolation for Sparse View Reconstruction
Technische Universität München（慕尼黑工业大学）；Waygate Technologies, Baker Hughes Digital Solutions GmbH（韦盖特技术，贝克休斯数字解决方案有限公司）；Technische Universität Darmstadt（达姆施塔特工业大学）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）
Rensselaer Polytechnic Institute（伦斯勒理工学院）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；Niskayuna High School（尼斯卡尤纳高中）
Cancer patients have a higher risk of cardiovascular disease (CVD) mortality than the general population. Low dose computed tomography (LDCT) for lung cancer screening offers an opportunity for simultaneous CVD risk estimation in at-risk patients. Our deep learning CVD risk prediction model, trained with 30,286 LDCTs from the National Lung Cancer Screening Trial, achieved an area under the curve (AUC) of 0.871 on a separate test set of 2,085 subjects and identified patients with high CVD mortality risks (AUC of 0.768). We validated our model against ECG-gated cardiac CT based markers, includin…
An efficient scalable data representation is an important task especially in the medical area, e.g. for volumes from Computed Tomography (CT) or Magnetic Resonance Tomography (MRT), when a downscaled version of the original signal is needed. Image and video coders based on wavelet transforms provide an adequate way to naturally achieve scalability. This paper presents a new approach for improving the visual quality of the lowpass band by using a novel graph-based method for motion compensation, which is an important step considering data compression. We compare different kinds of neighborhoods…
Computed Tomography (CT)-derived Cardiovascular Flow Estimation Using Physics-Informed Neural Networks Improves with Sinogram-based Training: A Simulation Study
Dept. of Bioengineering, University of California San Diego（加州大学圣地亚哥分校生物工程系）；Dept. of Computer Science Engineering, University of California San Diego（加州大学圣地亚哥分校计算机科学与工程系）；Dept. of Mechanical Engineering, Univ of Washington（华盛顿大学机械工程系）；Depts of Mechanical Engineering and Cardiology, Univ. of Washington（华盛顿大学机械工程与心内科系）；Depts. of Bioengineering, Radiology, University of California San Diego（加州大学圣地亚哥分校生物工程与放射学系）
PiMiX 2.02: Toward AI-Driven Data Fusion in Radiographic Imaging and Tomography
Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）；University of Michigan（密歇根大学）；NVIDIA（英伟达）；Brown University（布朗大学）
We present a Machine Learning-based method for tomographic reconstruction of dense layered objects, with range of projection angles limited to $\pm $10$^\circ$. Whereas previous approaches to phase tomography generally require two steps, first to retrieve phase projections from intensity projections and then perform tomographic reconstruction on the retrieved phase projections, in our work a physics-informed pre-processor followed by a Deep Neural Network (DNN) conduct the three-dimensional reconstruction directly from the intensity projections. We demonstrate this single-step method experimen…
Deep learning affords enormous opportunities to augment the armamentarium of biomedical imaging, albeit its design and implementation have potential flaws. Fundamentally, most deep learning models are driven entirely by data without consideration of any prior knowledge, which dramatically increases the complexity of neural networks and limits the application scope and model generalizability. Here we establish a geometry-informed deep learning framework for ultra-sparse 3D tomographic image reconstruction. We introduce a novel mechanism for integrating geometric priors of the imaging system. We…
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）
Virtual-mask Informed Prior for Sparse-view Dual-Energy CT Reconstruction
School of Mathematics and Computer Sciences, Nanchang University（南昌大学数学与计算机科学学院）；Department of Information Engineering, Nanchang University（南昌大学信息工程学院）
Synthetic optical coherence tomography angiographs for detailed retinal vessel segmentation without human annotations
Technical University of Munich（慕尼黑工业大学）；Imperial College London（帝国理工学院）；Helmholtz Zentrum Muenchen（亥姆霍兹慕尼黑中心）；University of Innsbruck（因斯布鲁克大学）；University of Oxford（牛津大学）；Moorfields Eye Hospital NHS Foundation Trust（摩尔菲尔德眼科医院国民保健服务基金会信托）；Boehringer Ingelheim Limited（勃林格殷格翰有限公司）；University College London（伦敦大学学院）；Cairo University（开罗大学）；Watany Eye Hospital（瓦塔尼眼科医院）；Heidelberg Engineering GmbH（海德堡工程有限公司）；University of Zurich（苏黎世大学）
Swarthmore College（斯沃斯莫尔学院）；Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）；National Renewable Energy Laboratory（国家可再生能源实验室）
Computed tomography has propelled scientific advances in fields from biology to materials science. This technology allows for the elucidation of 3-dimensional internal structure by the attenuation of x-rays through an object at different rotations relative to the beam. By imaging 2-dimensional projections, a 3-dimensional object can be reconstructed through a computational algorithm. Imaging at a greater number of rotation angles allows for improved reconstruction. However, taking more measurements increases the x-ray dose and may cause sample damage. Deep neural networks have been used to tra…
