A universal biventricular coordinate system incorporating valve annuli: Validation in congenital heart disease.

Universal coordinate systems have been proposed to facilitate anatomic registration between three-dimensional images, data and models of the ventricles of the heart. However, current universal ventricular coordinate systems do not account for the outflow tracts and valve annuli where the anatomy is complex. Here we propose an extension to the 'Cobiveco' biventricular coordinate system that also accounts for the intervalvular bridges of the base and provides a tool for anatomically consistent registration between widely varying biventricular shapes. CobivecoX uses a novel algorithm to separate intervalvular bridges and assign new coordinates, including an inflow-outflow coordinate, to describe local positions in these regions uniquely and consistently. Anatomic consistency of registration was validated using curated three-dimensional biventricular shape models derived from cardiac MRI measurements in normal hearts and hearts from patients with congenital heart diseases. This new method allows the advantages of universal cardiac coordinates to be used for three-dimensional ventricular imaging data and models that include the left and right ventricular outflow tracts and valve annuli.

Embedding high-dimensional Bayesian optimization via generative modeling: Parameter personalization of cardiac electrophysiological models.

The estimation of patient-specific tissue properties in the form of model parameters is important for personalized physiological models. Because tissue properties are spatially varying across the underlying geometrical model, it presents a significant challenge of high-dimensional (HD) optimization at the presence of limited measurement data. A common solution to reduce the dimension of the parameter space is to explicitly partition the geometrical mesh. In this paper, we present a novel concept that uses a generative variational auto-encoder (VAE) to embed HD Bayesian optimization into a low-dimensional (LD) latent space that represents the generative code of HD parameters. We further utilize VAE-encoded knowledge about the generative code to guide the exploration of the search space. The presented method is applied to estimating tissue excitability in a cardiac electrophysiological model in a range of synthetic and real-data experiments, through which we demonstrate its improved accuracy and substantially reduced computational cost in comparison to existing methods that rely on geometry-based reduction of the HD parameter space.

Optimal contrast-enhanced MRI image thresholding for accurate prediction of ventricular tachycardia using ex-vivo high resolution models.

Patient specific models created from contrast-enhanced (i.e. late-gadolinium, LGE) MRI images can be used for prediction of reentry location and clinical ablation planning. However, there is still a need for direct and systematic comparison between characteristics of ventricular tachycardia (VT) morphologies predicted in computational models and those acquired in clinical or experimental protocols. In this study, we aimed to: 1) assess the differences in VT morphologies predicted by modeling and recorded in experiments in terms of patterns and location of reentries, earliest and latest activation sites, and cycle lengths; and 2) define the optimal range of infarct tissue threshold values which provide best match between simulation and experimental results. To achieve these goals, we utilized LGE-MRI images from 4 swine hearts with inducible monomorphic VT. The images were segmented to identify non-infarcted myocardium, semi viable gray zone (GZ), and core scar based on pixel intensity. Several models were reconstructed from each LGE-MRI scan, with voxels of intensity between that of non-infarcted myocardium and 20-50% of the maximum intensity (in 10% increments) in the infarct region classified as GZ. VT induction was simulated in each model. Our simulation results showed that using GZ intensity thresholds of 20% or 30% resulted in the best match of simulated propagation patterns and reentry locations with those from the experiment. Overall, we matched 70% (7/10) morphologies for all the hearts. Our simulation shows that MRI-based computational models of hearts with myocardial infarction can accurately reproduce the majority of experimentally recorded post-infarction VTs.

Computational Modeling of Electrophysiology and Pharmacotherapy of Atrial Fibrillation: Recent Advances and Future Challenges.

The pathophysiology of atrial fibrillation (AF) is broad, with components related to the unique and diverse cellular electrophysiology of atrial myocytes, structural complexity, and heterogeneity of atrial tissue, and pronounced disease-associated remodeling of both cells and tissue. A major challenge for rational design of AF therapy, particularly pharmacotherapy, is integrating these multiscale characteristics to identify approaches that are both efficacious and independent of ventricular contraindications. Computational modeling has long been touted as a basis for achieving such integration in a rapid, economical, and scalable manner. However, computational pipelines for AF-specific drug screening are in their infancy, and while the field is progressing quite rapidly, major challenges remain before computational approaches can fill the role of workhorse in rational design of AF pharmacotherapies. In this review, we briefly detail the unique aspects of AF pathophysiology that determine requirements for compounds targeting AF rhythm control, with emphasis on delimiting mechanisms that promote AF triggers from those providing substrate or supporting reentry. We then describe modeling approaches that have been used to assess the outcomes of drugs acting on established AF targets, as well as on novel promising targets including the ultra-rapidly activating delayed rectifier potassium current, the acetylcholine-activated potassium current and the small conductance calcium-activated potassium channel. Finally, we describe how heterogeneity and variability are being incorporated into AF-specific models, and how these approaches are yielding novel insights into the basic physiology of disease, as well as aiding identification of the important molecular players in the complex AF etiology.

Quantifying the uncertainty in model parameters using Gaussian process-based Markov chain Monte Carlo in cardiac electrophysiology.

Model personalization requires the estimation of patient-specific tissue properties in the form of model parameters from indirect and sparse measurement data. Moreover, a low-dimensional representation of the parameter space is needed, which often has a limited ability to reveal the underlying tissue heterogeneity. As a result, significant uncertainty can be associated with the estimated values of the model parameters which, if left unquantified, will lead to unknown variability in model outputs that will hinder their reliable clinical adoption. Probabilistic estimation of model parameters, however, remains an unresolved challenge. Direct Markov Chain Monte Carlo (MCMC) sampling of the posterior distribution function (pdf) of the parameters is infeasible because it involves repeated evaluations of the computationally expensive simulation model. To accelerate this inference, one popular approach is to construct a computationally efficient surrogate and sample from this approximation. However, by sampling from an approximation, efficiency is gained at the expense of sampling accuracy. In this paper, we address this issue by integrating surrogate modeling of the posterior pdf into accelerating the Metropolis-Hastings (MH) sampling of the exact posterior pdf. It is achieved by two main components: (1) construction of a Gaussian process (GP) surrogate of the exact posterior pdf by actively selecting training points that allow for a good global approximation accuracy with a focus on the regions of high posterior probability; and (2) use of the GP surrogate to improve the proposal distribution in MH sampling, in order to improve the acceptance rate. The presented framework is evaluated in its estimation of the local tissue excitability of a cardiac electrophysiological model in both synthetic data experiments and real data experiments. In addition, the obtained posterior distributions of model parameters are interpreted in relation to the factors contributing to parameter uncertainty, including different low-dimensional representations of the parameter space, parameter non-identifiability, and parameter correlations.

Personalized virtual-heart technology for guiding the ablation of infarct-related ventricular tachycardia.

Ventricular tachycardia (VT), which can lead to sudden cardiac death, occurs frequently in patients with myocardial infarction. Catheter-based radiofrequency ablation of cardiac tissue has achieved only modest efficacy, owing to the inaccurate identification of ablation targets by current electrical mapping techniques, which can lead to extensive lesions and to a prolonged, poorly tolerated procedure. Here we show that personalized virtual-heart technology based on cardiac imaging and computational modelling can identify optimal infarct-related VT ablation targets in retrospective animal (5 swine) and human studies (21 patients) and in a prospective feasibility study (5 patients). We first assessed in retrospective studies (one of which included a proportion of clinical images with artifacts) the capability of the technology to determine the minimum-size ablation targets for eradicating all VTs. In the prospective study, VT sites predicted by the technology were targeted directly, without relying on prior electrical mapping. The approach could improve infarct-related VT ablation guidance, where accurate identification of patient-specific optimal targets could be achieved on a personalized virtual heart prior to the clinical procedure.

Spatially Adaptive Multi-Scale Optimization for Local Parameter Estimation in Cardiac Electrophysiology.

To obtain a patient-specific cardiac electro-physiological (EP) model, it is important to estimate the 3-D distributed tissue properties of the myocardium. Ideally, the tissue property should be estimated at the resolution of the cardiac mesh. However, such high-dimensional estimation faces major challenges in identifiability and computation. Most existing works reduce this dimension by partitioning the cardiac mesh into a pre-defined set of segments. The resulting low-resolution solutions have a limited ability to represent the underlying heterogeneous tissue properties of varying sizes, locations, and distributions. In this paper, we present a novel framework that, going beyond a uniform low-resolution approach, is able to obtain a higher resolution estimation of tissue properties represented by spatially non-uniform resolution. This is achieved by two central elements: 1) a multi-scale coarse-to-fine optimization that facilitates higher resolution optimization using the lower resolution solution and 2) a spatially adaptive decision criterion that retains lower resolution in homogeneous tissue regions and allows higher resolution in heterogeneous tissue regions. The presented framework is evaluated in estimating the local tissue excitability properties of a cardiac EP model on both synthetic and real data experiments. Its performance is compared with optimization using pre-defined segments. Results demonstrate the feasibility of the presented framework to estimate local parameters and to reveal heterogeneous tissue properties at a higher resolution without using a high number of unknowns.

A feasibility study of arrhythmia risk prediction in patients with myocardial infarction and preserved ejection fraction.

AIM: To predict arrhythmia susceptibility in myocardial infarction (MI) patients with left ventricular ejection fraction (LVEF) >35% using a personalized virtual heart simulation approach. METHODS AND RESULTS: A total of four contrast enhanced magnetic resonance imaging (MRI) datasets of patient hearts with MI and average LVEF of 44.0 ± 2.6% were used in this study. Because of the preserved LVEF, the patients were not indicated for implantable cardioverter defibrillator (ICD) insertion. One patient had spontaneous ventricular tachycardia (VT) prior to the MRI scan; the others had no arrhythmic events. Simulations of arrhythmia susceptibility were blind to clinical outcome. Models were constructed from patient MRI images segmented to identify myocardium, grey zone, and scar based on pixel intensity. Grey zone was modelled as having altered electrophysiology. Programmed electrical stimulation (PES) was performed to assess VT inducibility from 19 bi-ventricular sites in each heart model. Simulations successfully predicted arrhythmia risk in all four patients. For the patient with arrhythmic event, in-silico PES resulted in VT induction. Simulations correctly predicted that VT was non-inducible for the three patients with no recorded VT events. CONCLUSIONS: Results demonstrate that the personalized virtual heart simulation approach may provide a novel risk stratification modality to non-invasively and effectively identify patients with LVEF >35% who could benefit from ICD implantation.

Optogenetic defibrillation terminates ventricular arrhythmia in mouse hearts and human simulations.

Ventricular arrhythmias are among the most severe complications of heart disease and can result in sudden cardiac death. Patients at risk currently receive implantable defibrillators that deliver electrical shocks to terminate arrhythmias on demand. However, strong electrical shocks can damage the heart and cause severe pain. Therefore, we have tested optogenetic defibrillation using expression of the light-sensitive channel channelrhodopsin-2 (ChR2) in cardiac tissue. Epicardial illumination effectively terminated ventricular arrhythmias in hearts from transgenic mice and from WT mice after adeno-associated virus-based gene transfer of ChR2. We also explored optogenetic defibrillation for human hearts, taking advantage of a recently developed, clinically validated in silico approach for simulating infarct-related ventricular tachycardia (VT). Our analysis revealed that illumination with red light effectively terminates VT in diseased, ChR2-expressing human hearts. Mechanistically, we determined that the observed VT termination is due to ChR2-mediated transmural depolarization of the myocardium, which causes a block of voltage-dependent Na+ channels throughout the myocardial wall and interrupts wavefront propagation into illuminated tissue. Thus, our results demonstrate that optogenetic defibrillation is highly effective in the mouse heart and could potentially be translated into humans to achieve nondamaging and pain-free termination of ventricular arrhythmia.

Computational rabbit models to investigate the initiation, perpetuation, and termination of ventricular arrhythmia.

Current understanding of cardiac electrophysiology has been greatly aided by computational work performed using rabbit ventricular models. This article reviews the contributions of multiscale models of rabbit ventricles in understanding cardiac arrhythmia mechanisms. This review will provide an overview of multiscale modeling of the rabbit ventricles. It will then highlight works that provide insights into the role of the conduction system, complex geometric structures, and heterogeneous cellular electrophysiology in diseased and healthy rabbit hearts to the initiation and maintenance of ventricular arrhythmia. Finally, it will provide an overview on the contributions of rabbit ventricular modeling on understanding the mechanisms underlying shock-induced defibrillation.

Arrhythmia risk stratification of patients after myocardial infarction using personalized heart models.

Sudden cardiac death (SCD) from arrhythmias is a leading cause of mortality. For patients at high SCD risk, prophylactic insertion of implantable cardioverter defibrillators (ICDs) reduces mortality. Current approaches to identify patients at risk for arrhythmia are, however, of low sensitivity and specificity, which results in a low rate of appropriate ICD therapy. Here, we develop a personalized approach to assess SCD risk in post-infarction patients based on cardiac imaging and computational modelling. We construct personalized three-dimensional computer models of post-infarction hearts from patients' clinical magnetic resonance imaging data and assess the propensity of each model to develop arrhythmia. In a proof-of-concept retrospective study, the virtual heart test significantly outperformed several existing clinical metrics in predicting future arrhythmic events. The robust and non-invasive personalized virtual heart risk assessment may have the potential to prevent SCD and avoid unnecessary ICD implantations.

Exploring susceptibility to atrial and ventricular arrhythmias resulting from remodeling of the passive electrical properties in the heart: a simulation approach.

Under diseased conditions, remodeling of the cardiac tissue properties ("passive properties") takes place; these are aspects of electrophysiological behavior that are not associated with active ion transport across cell membranes. Remodeling of the passive electrophysiological properties most often results from structural remodeling, such as gap junction down-regulation and lateralization, fibrotic growth infiltrating the myocardium, or the development of an infarct scar. Such structural remodeling renders atrial or ventricular tissue as a major substrate for arrhythmias. The current review focuses on these aspects of cardiac arrhythmogenesis. Due to the inherent complexity of cardiac arrhythmias, computer simulations have provided means to elucidate interactions pertinent to this spatial scale. Here we review the current state-of-the-art in modeling atrial and ventricular arrhythmogenesis as arising from the disease-induced changes in the passive tissue properties, as well as the contributions these modeling studies have made to our understanding of the mechanisms of arrhythmias in the heart. Because of the rapid advance of structural imaging methodologies in cardiac electrophysiology, we chose to present studies that have used such imaging methodologies to construct geometrically realistic models of cardiac tissue, or the organ itself, where the regional remodeling properties of the myocardium can be represented in a realistic way. We emphasize how the acquired knowledge can be used to pave the way for clinical applications of cardiac organ modeling under the conditions of structural remodeling.

Sodium current reduction unmasks a structure-dependent substrate for arrhythmogenesis in the normal ventricles.

BACKGROUND: Organ-scale arrhythmogenic consequences of source-sink mismatch caused by impaired excitability remain unknown, hindering the understanding of pathophysiology in disease states like Brugada syndrome and ischemia. OBJECTIVE: We sought to determine whether sodium current (INa) reduction in the structurally normal heart unmasks a regionally heterogeneous substrate for the induction of sustained arrhythmia by premature ventricular contractions (PVCs). METHODS: We conducted simulations in rabbit ventricular computer models with 930 unique combinations of PVC location (10 sites) and coupling interval (250-400 ms), INa reduction (30 or 40% of normal levels), and post-PVC sinus rhythm (arrested or persistent). Geometric characteristics and source-sink mismatch were quantitatively analyzed by calculating ventricular wall thickness and a newly formulated 3D safety factor (SF), respectively. RESULTS: Reducing INa to 30% of its normal level created a substrate for sustained arrhythmia induction by establishing large regions of critical source-sink mismatch (SF<1) for ectopic wavefronts propagating from thin to thick tissue. In the same simulations but with 40% of normal INa, PVCs did not induce reentry because the volume of tissue with SF<1 was >95% smaller. Likewise, when post-PVC sinus activations were persistent instead of arrested, no ectopic excitations initiated sustained reentry because sinus activation breakthroughs engulfed the excitable gap. CONCLUSION: Our new SF formulation can quantify ectopic wavefront propagation robustness in geometrically complex 3D tissue with impaired excitability. This novel methodology was applied to show that INa reduction precipitates source-sink mismatch, creating a potent substrate for sustained arrhythmia induction by PVCs originating near regions of ventricular wall expansion, such as the RV outflow tract.

Computational cardiology: how computer simulations could be used to develop new therapies and advance existing ones.

This article reviews the latest developments in computational cardiology. It focuses on the contribution of cardiac modelling to the development of new therapies as well as the advancement of existing ones for cardiac arrhythmias and pump dysfunction. Reviewed are cardiac modelling efforts aimed at advancing and optimizing existent therapies for cardiac disease (defibrillation, ablation of ventricular tachycardia, and cardiac resynchronization therapy) and at suggesting novel treatments, including novel molecular targets, as well as efforts to use cardiac models in stratification of patients likely to benefit from a given therapy, and the use of models in diagnostic procedures.

Three-dimensional mechanisms of increased vulnerability to electric shocks in myocardial infarction: altered virtual electrode polarizations and conduction delay in the peri-infarct zone.

Defibrillation efficacy is decreased in infarcted hearts, but the mechanisms by which infarcted hearts are more vulnerable to electric shocks than healthy hearts remain poorly understood. The goal of this study was to provide insight into the 3D mechanisms for the increased vulnerability to electric shocks in infarcted hearts. We hypothesized that changes in virtual electrode polarizations (VEPs) and propagation delay through the peri-infarct zone (PZ) were responsible. We developed a micro anatomically detailed rabbit ventricular model with chronic myocardial infarction from magnetic resonance imaging and enriched the model with data from optical mapping experiments. We further developed a control model without the infarct. The simulation protocol involved apical pacing followed by biphasic shocks. Simulation results from both models were compared.The upper limit of vulnerability(ULV) was 8 V cm(-1) in the infarction model and 4 V cm(-1) in the control model. VEPs were less pronounced in the infarction model, providing a larger excitable area for postshock propagation but smaller transmembrane potential gradients to initiate new wavefronts. Initial post-shock transmural activation occurred at a later time in the infarction model, and the PZ served to delay propagation in subsequent beats. The presence of the PZ was found to be responsible for the increased vulnerability.

Susceptibility to arrhythmia in the infarcted heart depends on myofibroblast density.

Fibroblasts are electrophysiologically quiescent in the healthy heart. Evidence suggests that remodeling following myocardial infarction may include coupling of myofibroblasts (Mfbs) among themselves and with myocytes via gap junctions. We use a magnetic resonance imaging-based, three-dimensional computational model of the chronically infarcted rabbit ventricles to characterize the arrhythmogenic substrate resulting from Mfb infiltration as a function of Mfb density. Mfbs forming gap junctions were incorporated into both infarct regions, the periinfarct zone (PZ) and the scar; six scenarios were modeled: 0%, 10%, and 30% Mfbs in the PZ, with either 80% or 0% Mfbs in the scar. Ionic current remodeling in PZ was also included. All preparations exhibited elevated resting membrane potential within and near the PZ and action potential duration shortening throughout the ventricles. The unique combination of PZ ionic current remodeling and different degrees of Mfb infiltration in the infarcted ventricles determines susceptibility to arrhythmia. At low densities, Mfbs do not alter arrhythmia propensity; the latter arises predominantly from ionic current remodeling in PZ. At intermediate densities, Mfbs cause additional action potential shortening and exacerbate arrhythmia propensity. At high densities, Mfbs protect against arrhythmia by causing resting depolarization and blocking propagation, thus overcoming the arrhythmogenic effects of PZ ionic current remodeling.