Linwei Wang

Physiological-Model-Constrained Noninvasive Reconstruction of Volumetric Myocardial Transmembrane Potentials

Personalized noninvasive imaging of subject-specific cardiac electrical activity can guide and improve preventive diagnosis and treatment of cardiac arrhythmia. Compared to body surface potential (BSP) recordings and electrophysiological information reconstructed on heart surfaces, volumetric myocardial transmembrane potential (TMP) dynamics is of greater clinical importance in exhibiting arrhythmic details and arrythmogenic substrates inside the myocardium. This paper presents a physiological-model-constrained statistical framework to reconstruct volumetric TMP dynamics inside the 3-D myocardium from noninvasive BSP recordings. General knowledge of volumetric TMP activity is incorporated through the modeling of cardiac electrophysiological system, and is used to constrain TMP reconstruction. This physiological system is reformulated into a stochastic state-space representation to take into account model and data uncertainties, and nonlinear data assimilation is developed to estimate volumetric myocardial TMP dynamics from personal BSP data. Robustness of the presented framework to practical model and data errors is evaluated. Comparison of epicardial potential reconstructions with classical regularization-based approaches is performed on computational phantom regarding right bundle branch blocks. Further, phantom experiments on intramural focal activities and an initial real-data study on postmyocardial infarction demonstrate the potential of the framework in reconstructing local arrhythmic details and identifying arrhythmogenic substrates inside the myocardium.

Noninvasive Computational Imaging of Cardiac Electrophysiology for 3-D Infarct

Myocardial infarction (MI) creates electrophysiologically altered substrates that are responsible for ventricular ar rhythmias, such as tachycardia and fibrillation. The presence, size, location, and composition of infarct scar bear significant prognostic and therapeutic implications for individual subjects. We have developed a statistical physiological model-constrained framework that uses noninvasive body-surface-potential data and tomographic images to estimate subject-specific transmembrane potential (TMP) dynamics inside the 3-D myocardium. In this paper, we adapt this framework for the purpose of noninvasive imaging, detection, and quantification of 3-D scar mass for postMI patients: the framework requires no prior knowledge of MI and converges to final subject-specific TMP estimates after several passes of estimation with intermediate feedback; based on the primary features of the estimated spatiotemporal TMP dynamics, we provide 3-D imaging of scar tissue and quantitative evaluation of scar location and extent. Phantom experiments were performed on a computational model of realistic heart-torso geometry, considering 87 transmural infarct scars of different sizes and locations inside the myocardium, and 12 compact infarct scars (extent between 10% and 30%) at different transmural depths. Real data experiments were carried out on BSP and magnetic resonance imaging (MRI) data from four postMI patients, validated by gold standards and existing results. This framework shows unique advantage of noninvasive, quantitative, computational imaging of subject-specific TMP dynamics and infarct mass of the 3-D myocardium, with the potential to reflect details in the spatial structure and tissue composition/heterogeneity of 3-D infarct scar.

Inter-Model Consistency and Complementarity: Learning from ex-vivo Imaging and Electrophysiological Data towards an Integrated Understanding of Cardiac Physiology

Computational models of the heart at various scales and levels of complexity have been independently developed, parameterised and validated using a wide range of experimental data for over four decades. However, despite remarkable progress, the lack of coordinated efforts to compare and combine these computational models has limited their impact on the numerous open questions in cardiac physiology. To address this issue, a comprehensive dataset has previously been made available to the community that contains the cardiac anatomy and fibre orientations from magnetic resonance imaging as well as epicardial transmembrane potentials from optical mapping measured on a perfused ex-vivo porcine heart. This data was used to develop and customize four models of cardiac electrophysiology with different level of details, including a personalized fast conduction Purkinje system, a maximum a posteriori estimation of the 3D distribution of transmembrane potential, the personalization of a simplified reaction-diffusion model, and a detailed biophysical model with generic conduction parameters. This study proposes the integration of these four models into a single modelling and simulation pipeline, after analyzing their common features and discrepancies. The proposed integrated pipeline demonstrates an increase prediction power of depolarization isochrones in different pacing conditions.

Meshfree implementation of individualized active cardiac dynamics.

The cardiac physiome model has been proven to be useful for cardiac simulation, and has been more recently utilized to medical image analysis. To perform individualized analysis, structural images are necessary to provide subject-specific cardiac geometries. Although finite element methods have been extensively used for the spatial discretization of the myocardium, their complicated meshing procedures and element-based interpolation functions often result in algorithms which are either easy to implement but numerically inaccurate, or accurate but labor-intensive. In consequence, we have adopted the meshfree platform which provides element-free approximations for computational cardiology. Complicated volume meshing procedures are excluded, and no re-meshing is needed for improving spatial accuracy when deformation occurs. Furthermore, the polynomial bases for spatial approximation are not limited by the element structure. As a result, the meshfree platform is more adaptive to different cardiac geometries and thus beneficial to individualized analysis. In this paper, the cardiac physiome model tailored for medical image analysis is presented with its detailed 3D implementation using the meshfree methods. Experiments were performed to compare the meshfree methods with the finite element methods, and simulations were done on a cubical object to investigate the local behaviors of the cardiac physiome model, and on a human heart geometry extracted from a magnetic resonance image to verify its physiological plausibility.

Transmural Imaging of Ventricular Action Potentials and Post-Infarction Scars in Swine Hearts

The problem of using surface data to reconstruct transmural electrophysiological (EP) signals is intrinsically ill-posed without a unique solution in its unconstrained form. Incorporating physiological spatiotemporal priors through probabilistic integration of dynamic EP models, we have previously developed a Bayesian approach to transmural electrophysiological imaging (TEPI) using body-surface electrocardiograms. In this study, we generalize TEPI to using electrical signals collected from heart surfaces, and we test its feasibility on two pre-clinical swine models provided through the STACOM 2011 EP simulation Challenge. Since this new application of TEPI does not require whole-body imaging, there may be more immediate potential in EP laboratories where it could utilize catheter mapping data and produce transmural information for therapy guidance. Another focus of this study is to investigate the consistency among three modalities in delineating scar after myocardial infarction: TEPI, electroanatomical voltage mapping (EAVM), and magnetic resonance imaging (MRI). Our preliminary data demonstrate that, compared to the low-voltage scar area in EAVM, the 3-D electrical scar volume detected by TEPI is more consistent with anatomical scar volume delineated in MRI. Furthermore, TEPI could complement anatomical imaging by providing EP functional features related to both scar and healthy tissue.

Physiological Fusion of Functional and Structural Images for Cardiac Deformation Recovery

The recent advances in meaningful constraining models have resulted in increasingly useful quantitative information recovered from cardiac images. Nevertheless, as most frameworks utilize either functional or structural images, the analyses cannot benefit from the complementary information provided by the other image sources. To better characterize subject-specific cardiac physiology and pathology, data fusion of multiple image sources is essential. Traditional image fusion strategies are performed by fusing information of commensurate images through various mathematical operators. Nevertheless, when image data are dissimilar in physical nature and spatiotemporal quantity, such approaches may not provide meaningful connections between different data. In fact, as different image sources provide partial measurements of the same cardiac system dynamics, it is more natural and suitable to utilize cardiac physiological models for the fusions. Therefore, we propose to use the cardiac physiome model as the central link to fuse functional and structural images for more subject-specific cardiac deformation recovery through state-space filtering. Experiments were performed on synthetic and real data for the characteristics and potential clinical applicability of our framework, and the results show an increase of the overall subject specificity of the recovered deformations.

Imaging of 3d cardiac electrical activity: a model-based recovery framework

We present a model-based framework for imaging 3D cardiac transmembrane potential (TMP) distributions from body surface potential (BSP) measurements. Based on physiologically motivated modeling of the spatiotemporal evolution of TMPs and their projection to body surface, the cardiac electrophysiology is modeled as a stochastic system with TMPs as the latent dynamics and BSPs as external measurements. Given the patient-specific data from BSP measurements and tomographic medical images, the inverse problem of electrocardiography (IECG) is solved via state estimation of the underlying system, using the unscented Kalman filtering (UKF) for data assimilation. By incorporating comprehensive a priori physiological information, the framework enables direct recovery of intracardiac electrophysiological events free from commonly used physical equivalent cardiac sources, and delivers accurate, robust, and fast converging results under different noise levels and types. Experiments concerning individual variances and pathologies are also conducted to verify its feasibility in patient-specific applications.

Noninvasive transmural electrophysiological imaging based on minimization of total-variation functional.

While tomographic imaging of cardiac structure and kinetics has improved substantially, electrophysiological mapping of the heart is still restricted to the surface with little or no depth information beneath. The progress in reconstructing 3-D action potential from surface voltage data has been hindered by the intrinsic ill-posedness of the problem and the lack of a unique solution in the absence of prior assumptions. In this work, we propose a novel adaption of the total-variation (TV) prior to exploit the unique spatial property of transmural action potential of being piecewise smooth with a steep boundary (gradient) separating depolarized and repolarized regions. We present a variational TV-prior instead of a common discrete TV-prior for improved robustness to mesh resolution, and solve the TV-minimization by a sequence of weighted, first-order L2-norm minimization. In a large set of phantom experiments, the proposed method is shown to outperform existing quadratic methods in preserving the steep gradient of action potential along the border of infarcts, as well as in capturing the disruption to the normal path of electrical wavefronts. Real-data experiments also further demonstrate the potential of the proposed method in revealing the location and shape of infarcts when quadratic methods fail to do so.

Lp-Norm Regularization in Volumetric Imaging of Cardiac Current Sources

Advances in computer vision have substantially improved our ability to analyze the structure and mechanics of the heart. In comparison, our ability to observe and analyze cardiac electrical activities is much limited. The progress to computationally reconstruct cardiac current sources from noninvasive voltage data sensed on the body surface has been hindered by the ill-posedness and the lack of a unique solution of the reconstruction problem. Common L2- and L1-norm regularizations tend to produce a solution that is either too diffused or too scattered to reflect the complex spatial structure of current source distribution in the heart. In this work, we propose a general regularization with Lp-norm (1 < p < 2) constraint to bridge the gap and balance between an overly smeared and overly focal solution in cardiac source reconstruction. In a set of phantom experiments, we demonstrate the superiority of the proposed Lp-norm method over its L1 and L2 counterparts in imaging cardiac current sources with increasing extents. Through computer-simulated and real-data experiments, we further demonstrate the feasibility of the proposed method in imaging the complex structure of excitation wavefront, as well as current sources distributed along the postinfarction scar border. This ability to preserve the spatial structure of source distribution is important for revealing the potential disruption to the normal heart excitation.

Electrocardiographic simulation on personalised heart-torso structures using coupled meshfree-BEM platform

The foremost premise for the success of noninvasive volumetric myocardial Transmembrane (TMP) imaging from Body Surface Potential (BSP) recordings is a realistic yet efficient TMP-to-BSP mapping model that balances model accuracy with reconstruction feasibility. This papers presents a novel coupled meshfree-BEM platform to this forward electrocardiographic modelling. Its numerical accuracy and convergence is quantitatively assessed against analytical solutions on a synthetic geometry. Electrocardiographic simulations of various cardiac conditions on personalised heart-torso structures are consistent with existent experimental studies. In further real data validations on three post myocardial infarction patients, simulated BSP exhibit high accuracy compared to measured BSP.