Ali Pashaei

OpenCMISS: A multi-physics & multi-scale computational infrastructure for the VPH/Physiome project

The VPH/Physiome Project is developing the model encoding standards CellML (cellml.org) and FieldML (fieldml.org) as well as web-accessible model repositories based on these standards (models.physiome.org). Freely available open source computational modelling software is also being developed to solve the partial differential equations described by the models and to visualise results. The OpenCMISS code (opencmiss.org), described here, has been developed by the authors over the last six years to replace the CMISS code that has supported a number of organ system Physiome projects. OpenCMISS is designed to encompass multiple sets of physical equations and to link subcellular and tissue-level biophysical processes into organ-level processes. In the Heart Physiome project, for example, the large deformation mechanics of the myocardial wall need to be coupled to both ventricular flow and embedded coronary flow, and the reaction-diffusion equations that govern the propagation of electrical waves through myocardial tissue need to be coupled with equations that describe the ion channel currents that flow through the cardiac cell membranes. In this paper we discuss the design principles and distributed memory architecture behind the OpenCMISS code. We also discuss the design of the interfaces that link the sets of physical equations across common boundaries (such as fluid-structure coupling), or between spatial fields over the same domain (such as coupled electromechanics), and the concepts behind CellML and FieldML that are embodied in the OpenCMISS data structures. We show how all of these provide a flexible infrastructure for combining models developed across the VPH/Physiome community.

Relationship between endocardial activation sequences defined by high-density mapping to early septal contraction (septal flash) in patients with left bundle branch block undergoing cardiac resynchronization therapy

AIMS Early inward motion and thickening/thinning of the ventricular septum associated with left bundle branch block is known as the septal flash (SF). Correction of SF corresponds to response to cardiac resynchronization therapy (CRT). We hypothesized that SF was associated with a specific left ventricular (LV) activation pattern predicting a favourable response to CRT. We sought to characterize the spatio-temporal relationship between electrical and mechanical events by directly comparing non-contact mapping (NCM), acute haemodynamics, and echocardiography. METHODS AND RESULTS Thirteen patients (63 ± 10 years, 10 men) with severe heart failure (ejection fraction 22.8 ± 5.8%) awaiting CRT underwent echocardiography and NCM pre-implant. Presence and extent of SF defined visually and with M-mode was fused with NCM bulls eye plots of endocardial activation patterns. LV-dP/dt(max) was measured during different pacing modes. Five patients had a large SF, four small SF, and four no SF. Large SF patients had areas of conduction block in non-infarcted regions, whereas those with small or no SF did not. Patients with large SF had greater acute response to LV and biventricular (BIV) pacing vs. those with small/no SF (% increase dP/dt 28 ± 14 vs. 11 ± 19% for LV pacing and 42 ± 28 vs. 22 ± 21% for BIV pacing) (P < 0.05). This translated into a more favourable chronic response to CRT. The lines of conduction block disappeared with LV/BIV pacing while remaining with right ventricle pacing. CONCLUSION A strong association exists between electrical activation and mechanical deformation of the septum. Correction of both mechanical synchrony and the functional conduction block by CRT may explain the favourable response in patients with SF.

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.

Understanding the mechanisms amenable to CRT response: from pre-operative multimodal image data to patient-specific computational models

This manuscript describes our recent developments towards better understanding of the mechanisms amenable to cardiac resynchronization therapy response. We report the results from a full multimodal dataset corresponding to eight patients from the euHeart project. The datasets include echocardiography, MRI and electrophysiological studies. We investigate two aspects. The first one focuses on pre-operative multimodal image data. From 2D echocardiography and 3D tagged MRI images, we compute atlas based dyssynchrony indices. We complement these indices with presence and extent of scar tissue and correlate them with CRT response. The second one focuses on computational models. We use pre-operative imaging to generate a patient-specific computational model. We show results of a fully automatic personalized electromechanical simulation. By case-per-case discussion of the results, we highlight the potential and key issues of this multimodal pipeline for the understanding of the mechanisms of CRT response and a better patient selection.

Numerical simulation of blood flow in the left ventricle and aortic sinus using magnetic resonance imaging and computational fluid dynamics

Understanding cardiac blood flow patterns has many applications in analysing haemodynamics and for the clinical assessment of heart function. In this study, numerical simulations of blood flow in a patient-specific anatomical model of the left ventricle (LV) and the aortic sinus are presented. The realistic 3D geometry of both LV and aortic sinus is extracted from the processing of magnetic resonance imaging (MRI). Furthermore, motion of inner walls of LV and aortic sinus is obtained from cine-MR image analysis and is used as a constraint to a numerical computational fluid dynamics (CFD) model based on the moving boundary approach. Arbitrary Lagrangian–Eulerian finite element method formulation is used for the numerical solution of the transient dynamic equations of the fluid domain. Simulation results include detailed flow characteristics such as velocity, pressure and wall shear stress for the whole domain. The aortic outflow is compared with data obtained by phase-contrast MRI. Good agreement was found between simulation results and these measurements.

Statistical Personalization of Ventricular Fiber Orientation Using Shape Predictors

This paper presents a predictive framework for the statistical personalization of ventricular fibers. To this end, the relationship between subject-specific geometry of the left (LV) and right ventricles (RV) and fiber orientation is learned statistically from a training sample of ex vivo diffusion tensor imaging datasets. More specifically, the axes in the shape space which correlate most with the myocardial fiber orientations are extracted and used for prediction in new subjects. With this approach and unlike existing fiber models, inter-subject variability is taken into account to generate latent shape predictors that are statistically optimal to estimate fiber orientation at each individual myocardial location. The proposed predictive model was applied to the task of personalizing fibers in 10 canine subjects. The results indicate that the ventricular shapes are good predictors of fiber orientation, with an improvement of 11.4% in accuracy over the average fiber model.

Fast Multiscale Modeling of Cardiac Electrophysiology Including Purkinje System

In this paper, we present a modeling methodology to couple the cardiac conduction system to cardiac myocytes through a model of Purkinje-ventricular junctions to yield fast and realistic electrical activation of the ventricles. A patient-specific biventricular geometry is obtained from processing computed tomography scan data. A one-manifold implementation of the fast marching method based on Eikonal-type equations is used for modeling heart electrophysiology, which facilitates the multiscale 1-D-3-D coupling at very low computational costs. The method is illustrated in in-silico experiments where we analyze and compare alternative pacing strategies on the same patient-specific anatomy. We also show very good agreement between the results from the proposed approach and more detailed and comprehensive biophysical models for modeling cardiac electrophysiology. The effect of atrioventricular delay on the distribution of activation time in myocardium is studied with two experiments. Given the reasonable computational times and realistic activation sequences provided by our method, it can have an important clinical impact on the selection of optimal implantation sites of pacing leads or placement of ablation catheters tip in the context of cardiac rhythm management therapies.

Effect of Statistically Derived Fiber Models on the Estimation of Cardiac Electrical Activation

Myocardial fiber orientation plays a critical role in the electrical activation and subsequent contraction of the heart. To increase the clinical potential of electrophysiological (EP) simulation for the study of cardiac phenomena and the planning of interventions, accurate personalization of the fibers is a necessary yet challenging task. Due to the difficulties associated with the in vivo imaging of cardiac fiber structure, researchers have developed alternative techniques to personalize fibers. Thus far, cardiac simulation was performed mainly based on rule-based fiber models. More recently, there has been a significant interest in data-driven and statistically derived fiber models. In particular, our predictive method in [1] allows us to estimate the unknown subject-specific fiber orientation based on the more easily available shape information. The aim of this work is to estimate the effect of using such statistical predictive models for the estimation of cardiac electrical activation times and patterns. To this end, we perform EP simulations based on a database of ten canine ex vivo diffusion tensor imaging (DTI) datasets that include normal and failing cases. To assess the strength of the fiber models under varying conditions, we consider both sinus rhythm and biventricular pacing simulations. The results show that 1) the statistically derived fibers improve the estimation of the local activation times by an average of 53.7% over traditional rule-based models, and that 2) the obtained electrical activations are consistently similar to those of the DTI-based fibers.

Personalization of fast conduction Purkinje system in eikonal-based electrophysiological models with optical mapping data

We present a pipeline for the personalization of model-based Purkinje fast conduction system using fast electrophysiological models and optical mapping data acquired from ex-vivo porcine hearts. The regional density of the Purkinje terminals as well as the latest endocardial activation time were the parameters personalized in an iterative procedure maximizing the similarity between the outcome of the electrophysiological simulations and measurements obtained from optical mapping data. We used a fast wave-front Eikonal-based electrophysiological model that generated the depolarization time maps that were subsequently compared with measurements at each iteration of the optimization stage. The pacing site given by the experimental data and the optimized Purkinje system were introduced into the electrophysiological model. We obtained a regional distribution of Purkinje end-terminals in agreement with findings in the literature. Nevertheless, remaining differences between simulations and measurements after personalization suggest that epicardial data obtained from optical mapping data might not be sufficient to optimize the Purkinje system, which is basically located at the endocardium. On the other hand, the developed pipeline could also be used with endocardial data on electrical activation provided by non-contact or contact mapping system.

Sensitivity analysis of mesh warping and subsampling strategies for generating large scale electrophysiological simulation data

The analysis of large-scale simulation data from virtual populations can be effective to gain computational insight into disease mechanisms and treatment strategies, which can serve for generating hypotheses for and focusing subsequent clinical trials. This can be instrumental in shortening the critical path in medical product development and more cost-effective clinical trials. A previously published pipeline established point correspondence among volumetric meshes to enable meaningful statistics on cardiac electrophysiological simulations on the anatomical distribution of a large-scale virtual population. Thin Plate Splines (TPS), derived from surface deformations, were used to warp a template volumetric mesh, removing the costly operation of repeated volumetric meshing from the pipeline, but potentially at the cost of the volumetric mesh quality. In this work we compare (1) the influence of using TPS versus volumetric meshing of deformed surface meshes, and (2) the influence of surface mesh subsampling prior to the TPS computation. Our results suggest that warping of a template volumetric mesh introduces errors in electrophysiological simulation results of around 4 ms, while having computational times per mesh on the order of seconds, at surface subsampling rates of up to 80%.