A new three-dimensional finite-difference bidomain formulation for inhomogeneous anisotropic cardiac tissues

Bidomain modeling of electrical and mechanical properties of cardiac tissue

Throughout the history of cardiac research, there has been a clear need to establish mathematical models to complement experimental studies. In an effort to create a more complete picture of cardiac phenomena, the bidomain model was established in the late 1970s to better understand pacing and defibrillation in the heart. This mathematical model has seen ongoing use in cardiac research, offering mechanistic insight that could not be obtained from experimental pursuits. Introduced from a historical perspective, the origins of the bidomain model are reviewed to provide a foundation for researchers new to the field and those conducting interdisciplinary research. The interplay of theory and experiment with the bidomain model is explored, and the contributions of this model to cardiac biophysics are critically evaluated. Also discussed is the mechanical bidomain model, which is employed to describe mechanotransduction. Current challenges and outstanding questions in the use of the bidomain model are addressed to give a forward-facing perspective of the model in future studies.

Influence of Patient-Specific Head Modeling on EEG Source Imaging

Electromagnetic source imaging (ESI) techniques have become one of the most common alternatives for understanding cognitive processes in the human brain and for guiding possible therapies for neurological diseases. However, ESI accuracy strongly depends on the forward model capabilities to accurately describe the subject's head anatomy from the available structural data. Attempting to improve the ESI performance, we enhance the brain structure model within the individual-defined forward problem formulation, combining the head geometry complexity of the modeled tissue compartments and the prior knowledge of the brain tissue morphology. We validate the proposed methodology using 25 subjects, from which a set of magnetic-resonance imaging scans is acquired, extracting the anatomical priors and an electroencephalography signal set needed for validating the ESI scenarios. Obtained results confirm that incorporating patient-specific head models enhances the performed accuracy and improves the localization of focal and deep sources.

A Finite-Difference Solution for the EEG Forward Problem in Inhomogeneous Anisotropic Media

Accurate source localization of electroencephalographic (EEG) signals requires detailed information about the geometry and physical properties of head tissues. Indeed, these strongly influence the propagation of neural activity from the brain to the sensors. Finite difference methods (FDMs) are head modelling approaches relying on volumetric data information, which can be directly obtained using magnetic resonance (MR) imaging. The specific goal of this study is to develop a computationally efficient FDM solution that can flexibly integrate voxel-wise conductivity and anisotropy information. Given the high computational complexity of FDMs, we pay particular attention to attain a very low numerical error, as evaluated using exact analytical solutions for spherical volume conductor models. We then demonstrate the computational efficiency of our FDM numerical solver, by comparing it with alternative solutions. Finally, we apply the developed head modelling tool to high-resolution MR images from a real experimental subject, to demonstrate the potential added value of incorporating detailed voxel-wise conductivity and anisotropy information. Our results clearly show that the developed FDM can contribute to a more precise head modelling, and therefore to a more reliable use of EEG as a brain imaging tool.

Optimizing bipolar radiofrequency ablation treatment by means of pulsed currents

Given the high mortality rate, liver cancer is considered to be a difficult cancer to treat. Consequently, alternative strategies are being developed such as radiofrequency ablation (RFA). RFA applies radiofrequent currents leading to local heating of the tumoral tissue. Accurate numerical modeling contributes to a better knowledge of the physical phenomena and allows optimizations. In this work, the bipolar radiofrequency ablation technique is explored followed by an optimization by means of pulsed currents. Numerical results clearly show the larger ablation zones due to the pulsed currents. Hence, pulsed bipolar RFA increases the efficacy and has the potential to be incorporated in clinical practice.

Numerical study of magnetoacoustic signal generation with magnetic induction based on inhomogeneous conductivity anisotropy

Magnetoacoustic tomography with magnetic induction (MAT-MI) is a noninvasive imaging modality for generating electrical conductivity images of biological tissues with high spatial resolution. In this paper, we create a numerical model, including a permanent magnet, a coil, and a two-layer coaxial cylinder with anisotropic electrical conductivities, for the MAT-MI forward problem. We analyze the MAT-MI sources in two cases, on a thin conductive boundary layer and in a homogeneous medium, and then develop a feasible numerical approach to solve the MAT-MI sound source densities in the anisotropic conductive model based on finite element analysis of electromagnetic field. Using the numerical finite element method, we then investigate the magnetoacoustic effect of anisotropic conductivity under the inhomogeneous static magnetic field and inhomogeneous magnetic field, quantitatively compute the boundary source densities in the conductive model, and calculate the sound pressure. The anisotropic conductivity contributes to the distribution of the eddy current density, Lorentz force density, and acoustic signal. The proposed models and approaches provide a more realistic simulation environment for MAT-MI.

Orthogonal recursive bisection as data decomposition strategy for massively parallel cardiac simulations

We present the orthogonal recursive bisection algorithm that hierarchically segments the anatomical model structure into subvolumes that are distributed to cores. The anatomy is derived from the Visible Human Project, with electrophysiology based on the FitzHugh-Nagumo (FHN) and ten Tusscher (TT04) models with monodomain diffusion. Benchmark simulations with up to 16,384 and 32,768 cores on IBM Blue Gene/P and L supercomputers for both FHN and TT04 results show good load balancing with almost perfect speedup factors that are close to linear with the number of cores. Hence, strong scaling is demonstrated. With 32,768 cores, a 1000 ms simulation of full heart beat requires about 6.5 min of wall clock time for a simulation of the FHN model. For the largest machine partitions, the simulations execute at a rate of 0.548 s (BG/P) and 0.394 s (BG/L) of wall clock time per 1 ms of simulation time. To our knowledge, these simulations show strong scaling to substantially higher numbers of cores than reported previously for organ-level simulation of the heart, thus significantly reducing run times. The ability to reduce runtimes could play a critical role in enabling wider use of cardiac models in research and clinical applications.

A comparison of non-standard solvers for ODEs describing cellular reactions in the heart

Mathematical models for the electrical activity in cardiac cells are normally formulated as systems of ordinary differential equations (ODEs). The equations are nonlinear and describe processes occurring on a wide range of time scales. Under normal accuracy requirements, this makes the systems stiff and therefore challenging to solve numerically. As standard implicit solvers are difficult to implement, explicit solvers such as the forward Euler method are commonly used, despite their poor efficiency. Non-standard formulations of the forward Euler method, derived from the analytical solution of linear ODEs, can give significantly improved performance while maintaining simplicity of implementation. In this paper we study the performance of three non-standard methods on two different cell models with comparable complexity but very different stiffness characteristics.

Solvers for the cardiac bidomain equations

The bidomain equations are widely used for the simulation of electrical activity in cardiac tissue. They are especially important for accurately modeling extracellular stimulation, as evidenced by their prediction of virtual electrode polarization before experimental verification. However, solution of the equations is computationally expensive due to the fine spatial and temporal discretization needed. This limits the size and duration of the problem which can be modeled. Regardless of the specific form into which they are cast, the computational bottleneck becomes the repeated solution of a large, linear system. The purpose of this review is to give an overview of the equations and the methods by which they have been solved. Of particular note are recent developments in multigrid methods, which have proven to be the most efficient.

Reduced-order preconditioning for bidomain simulations

Simulations of the bidomain equations involve solving large, sparse, linear systems of the form Ax = b. Being an initial value problems, it is solved at every time step. Therefore, efficient solvers are essential to keep simulations tractable. Iterative solvers, especially the preconditioned conjugate gradient (PCG) method, are attractive since memory demands are minimized compared to direct methods, albeit at the cost of solution speed. However, a proper preconditioner can drastically speed up the solution process by reducing the number of iterations. In this paper, a novel preconditioner for the PCG method based on system order reduction using the Arnoldi method (A-PCG) is proposed. Large order systems, generated during cardiac bidomain simulations employing a finite element method formulation, are solved with the A-PCG method. Its performance is compared with incomplete LU (ILU) preconditioning. Results indicate that the A-PCG estimates an approximate solution considerably faster than the ILU, often within a single iteration. To reduce the computational demands in terms of memory and run time, the use of a cascaded preconditioner was suggested. The A-PCG was applied to quickly obtain an approximate solution, and subsequently a cheap iterative method such as successive overrelaxation (SOR) is applied to further refine the solution to arrive at a desired accuracy. The memory requirements are less than those of direct LU but more than ILU method. The proposed scheme is shown to yield significant speedups when solving time evolving systems.

Arnoldi preconditioning for solving large linear biomedical systems

Simulations of biomedical systems often involve solving large, sparse, linear systems of the form Ax = b. In initial value problems, this system is solved at every time step, so a quick solution is essential for tractability. Iterative solvers, especially preconditioned conjugate gradient, are attractive since memory demands are minimized compared to direct methods, albeit at a cost of solution speed. A proper preconditioner can drastically reduce computation and remains an area of active research. In this paper, we propose a novel preconditioner based on system order reduction using the Arnoldi method. Systems of orders up to a million, generated from a finite element method formulation of the elliptic portion of the bidomain equations, are solved with the new preconditioner and performance is compared with that of other preconditioners. Results indicate that the new method converges considerably faster, often within a single iteration. It also uses less memory than an incomplete LU decomposition (ILU). For solving a system repeatedly, the Arnoldi transformation must be continually recomputed, unlike ILU, but this can be done quickly. In conclusion, for solving a system once, the Arnoldi preconditioner offers a greatly reduced solution time, and for repeated solves, will still be faster than an ILU preconditioner.

Accelerating large cardiac bidomain simulations by arnoldi preconditioning

Bidomain simulations of cardiac systems often in volve solving large, sparse, linear systems of the form Ax=b. These simulations are computationally very expensive in terms of run time and memory requirements. Therefore, efficient solvers are essential to keep simulations tractable. In this paper, an efficient preconditioner for the conjugate gradient (CG) method based on system order reduction using the Arnoldi method (A-PCG) is explained. Large order systems generated during cardiac bidomain simulations using a finite element method formulation, are solved using the A-PCG method. Its performance is compared with incomplete LU (ILU) preconditioning. Results indicate that the A-PCG estimates an approximate solution considerably faster than the ILU, often within a single iteration. To reduce the computational demands in terms of memory and run time, the use of a cascaded preconditioner is suggested. The A-PCG can be applied to quickly obtain an approximate solution, subsequently a cheap iterative method such as successive overrelaxation (SOR) is applied to further refine the solution to arrive at a desired accuracy. The memory requirements are less than direct LU but more than ILU method. The proposed scheme is shown to yield significant speedups when solving time evolving systems.

A generalized finite difference method for modeling cardiac electrical activation on arbitrary, irregular computational meshes

A generalized finite difference (GFD) method is presented that can be used to solve the bi-domain equations modeling cardiac electrical activity. Classical finite difference methods have been applied by many researchers to the bi-domain equations. However, these methods suffer from the limitation of requiring computational meshes that are structured and orthogonal. Finite element or finite volume methods enable the bi-domain equations to be solved on unstructured meshes, although implementations of such methods do not always cater for meshes with varying element topology. The GFD method solves the bi-domain equations on arbitrary and irregular computational meshes without any need to specify element basis functions. The method is useful as it can be easily applied to activation problems using existing meshes that have originally been created for use by finite element or finite difference methods. In addition, the GFD method employs an innovative approach to enforcing nodal and non-nodal boundary conditions. The GFD method performs effectively for a range of two and three-dimensional test problems and when computing bi-domain electrical activation moving through a fully anisotropic three-dimensional model of canine ventricles.

A finite difference method with reciprocity used to incorporate anisotropy in electroencephalogram dipole source localization

Many implementations of electroencephalogram (EEG) dipole source localization neglect the anisotropical conductivities inherent to brain tissues, such as the skull and white matter anisotropy. An examination of dipole localization errors is made in EEG source analysis, due to not incorporating the anisotropic properties of the conductivity of the skull and white matter. First, simulations were performed in a 5 shell spherical head model using the analytical formula. Test dipoles were placed in three orthogonal planes in the spherical head model. Neglecting the skull anisotropy results in a dipole localization error of, on average, 13.73 mm with a maximum of 24.51 mm. For white matter anisotropy these values are 11.21 mm and 26.3 mm, respectively. Next, a finite difference method (FDM), presented by Saleheen and Kwong (1997 IEEE Trans. Biomed. Eng. 44 800-9), is used to incorporate the anisotropy of the skull and white matter. The FDM method has been validated for EEG dipole source localization in head models with all compartments isotropic as well as in a head model with white matter anisotropy. In a head model with skull anisotropy the numerical method could only be validated if the 3D lattice was chosen very fine (grid size < or = 2 mm).

A Finite Volume Method for Modeling Discontinuous Electrical Activation in Cardiac Tissue

This paper describes a finite volume method for modeling electrical activation in a sample of cardiac tissue using the bidomain equations. Microstructural features to the level of cleavage planes between sheets of myocardial fibers in the tissue are explicitly represented. The key features of this implementation compared to previous modeling are that it represents physical discontinuities without the implicit removal of intracellular volume and it generates linear systems of equations that are computationally efficient to construct and solve. Results obtained using this method highlight how the understanding of discontinuous activation in cardiac tissue can form a basis for better understanding defibrillation processes and experimental recordings.

Solving the heart mechanics equations with Newton and quasi Newton methods--a comparison

The non-linear elasticity equations of heart mechanics are solved while emulating the effects of a propagating activation wave. The dynamics of a 1 cm(3) slab of active cardiac tissue was simulated as the electrical wave traversed the muscular heart wall transmurally. The regular Newton (Newton-Raphson) method was compared to two modified Newton approaches, and also to a third approach that delayed update only of some selected Jacobian elements. In addition, the impact of changing the time step (0.01, 0.1 and 1 ms) and the relative non-linear convergence tolerance (10(-4), 10(-3) and 10(-2)) was investigated. Updating the Jacobian only when slow convergence occurred was by far the most efficient approach, giving time savings of 83-96%. For each of the four methods, CPU times were reduced by 48-90% when the time step was increased by a factor 10. Increasing the convergence tolerance by the same factor gave time savings of 3-71%. Different combinations of activation wave speed, stress rate and bulk modulus revealed that the fastest method became relatively even faster as stress rate and bulk modulus was decreased, while the activation speed had negligible influence in this respect.

Simulation of QRST Integral Maps With a Membrane-Based Computer Heart Model Employing Parallel Processing

The simulation of the propagation of electrical activity in a membrane-based realistic-geometry computer model of the ventricles of the human heart, using the governing monodomain reaction-diffusion equation, is described. Each model point is represented by the phase 1 Luo-Rudy membrane model, modified to represent human action potentials. A separate longer duration action potential was used for the M cells found in the ventricular midwall. Cardiac fiber rotation across the ventricular wall was implemented via an analytic equation, resulting in a spatially varying anisotropic conductivity tensor and, consequently, anisotropic propagation. Since the model comprises approximately 12.5 million points, parallel processing on a multiprocessor computer was used to cut down on simulation time. The simulation of normal activation as well as that of ectopic beats is described. The hypothesis that in situ electrotonic coupling in the myocardium can diminish the gradients of action-potential duration across the ventricular wall was also verified in the model simulations. Finally, the sensitivity of QRST integral maps to local alterations in action-potential duration was investigated.

Three-dimensional pseudospectral modelling of cardiac propagation in an inhomogeneous anisotropic tissue

Various investigators have used the monodomain model to study cardiac propagation behaviour. In many cases, the governing non-linear parabolic equation is solved using the finite-difference method. An adequate discretisation of cardiac tissue with realistic dimensions, however, often leads to a large model size that is computationally demanding. Recently, it has been demonstrated, for a two-dimensional homogeneous monodomain, that the Chebyshev pseudospectral method can offer higher computational efficiency than the finite-difference technique. Here, an extension of the pseudospectral approach to a three-dimensional inhomogeneous case with fibre rotation is presented. The unknown transmembrane potential is expanded in terms of Chebyshev polynomial trial functions, and the monodomain equation is enforced at the Gauss-Lobatto node points. The forward Euler technique is used to advance the solution in time. Numerical results are presented that demonstrate that the Chebyshev pseudospectral method offered an even larger improvement in computational performance over the finite-difference method in the three-dimensional case. Specifically, the pseudospectral method allowed the number of nodes to be reduced by approximately 85 times, while the same solution accuracy was maintained. Depending on the model size, simulations were performed with approximately 18-41 times less memory and approximately 99-169 times less CPU time.

Computational techniques for solving the bidomain equations in three dimensions

The bidomain equations are the most complete description of cardiac electrical activity. Their numerical solution is, however, computationally demanding, especially in three dimensions, because of the fine temporal and spatial sampling required. This paper methodically examines computational performance when solving the bidomain equations. Several techniques to speed up this computation are examined in this paper. The first step was to recast the equations into a parabolic part and an elliptic part. The parabolic part was solved by either the finite-element method (FEM) or the interconnected cable model model (ICCM). The elliptic equation was solved by FEM on a coarser grid than the parabolic problem and at a reduced frequency. The performance of iterative and direct linear equation system solvers was analyzed as well as the scalability and parallelizability of each method. Results indicate that the ICCM was twice as fast as the FEM for solving the parabolic problem, but when the total problem was considered, this resulted in only a 20% decrease in computation time. The elliptic problem could be solved on a coarser grid at one-quarter of the frequency at which the parabolic problem was solved and still maintain reasonable accuracy. Direct methods were faster than iterative methods by at least 50% when a good estimate of the extracellular potential was required. Parallelization over four processors was efficient only when the model comprised at least 500,000 nodes. Thus, it was possible to speed up solution of the bidomain equations by an order of magnitude with a slight decrease in accuracy.

Calibrated single-plunge bipolar electrode array for mapping myocardial vector fields in three dimensions during high-voltage transthoracic defibrillation

Mapping of the myocardial scalar electric potential during defibrillation is normally performed with unipolar electrodes connected to voltage dividers and a global potential reference. Unfortunately, vector potential gradients that are calculated from these data tend to exhibit a high sensitivity to measurement errors. This paper presents a calibrated single-plunge bipolar electrode array (EA) that avoids the error sensitivity of unipolar electrodes. The EA is triaxial, uses a local potential reference, and simultaneously measures all three components of the myocardial electric field vector. An electrode spacing of approximately 500 microm allows the EA to be direct-coupled to high-input-impedance, isolated, differential amplifiers and eliminates the need for voltage dividers. Calibration is performed with an electrolytic tank in which an accurately measured, uniform electric field is produced. For each EA, unique calibration matrices are determined which transform potential difference readings from the EA to orthogonal components of the electric field vector. Elements of the matrices are evaluated by least squares multiple regression analysis of data recorded during rotation of the electric field. The design of the electrolytic tank and electrode holder allows the electric field vector to be rotated globally with respect to the electrode axes. The calibration technique corrects for both field perturbation by the plunge electrode body and deviations from orthogonality of the electrode axes. A unique feature of this technique is that it eliminates the need for mechanical measurement of the electrode spacing. During calibration, only angular settings and voltages are recorded. For this study, ten EAs were calibrated and their root-mean-square (rms) errors evaluated. The mean of the vector magnitude rms errors over the set of ten EAs was 0.40% and the standard deviation 0.07%. Calibrated EAs were also tested for multisite mapping in four dogs during high-voltage transthoracic shocks.

Distributed computing for membrane-based modeling of action potential propagation

Action potential propagation simulations with physiologic membrane currents and macroscopic tissue dimensions are computationally expensive. We, therefore, analyzed distributed computing schemes to reduce execution time in workstation clusters by parallelizing solutions with message passing. Four schemes were considered in two-dimensional monodomain simulations with the Beeler-Reuter membrane equations. Parallel speedups measured with each scheme were compared to theoretical speedups, recognizing the relationship between speedup and code portions that executed serially. A data decomposition scheme based on total ionic current provided the best performance. Analysis of communication latencies in that scheme led to a load-balancing algorithm in which measured speedups at 89 +/- 2% and 75 +/- 8% of theoretical speedups were achieved in homogeneous and heterogeneous clusters of workstations. Speedups in this scheme with the Luo-Rudy dynamic membrane equations exceeded 3.0 with eight distributed workstations. Cluster speedups were comparable to those measured during parallel execution on a shared memory machine.

A numerically efficient model for simulation of defibrillation in an active bidomain sheet of myocardium

Presented here is an efficient algorithm for solving the bidomain equations describing myocardial tissue with active membrane kinetics. An analysis of the accuracy shows advantages of this numerical technique over other simple and therefore popular approaches. The modular structure of the algorithm provides the critical flexibility needed in simulation studies: fiber orientation and membrane kinetics can be easily modified. The computational tool described here is designed specifically to simulate cardiac defibrillation, i. e., to allow modeling of strong electric shocks applied to the myocardium extracellularly. Accordingly, the algorithm presented also incorporates modifications of the membrane model to handle the high transmembrane voltages created in the immediate vicinity of the defibrillation electrodes.

Two-dimensional Chebyshev pseudospectral modelling of cardiac propagation

Bidomain or monodomain modelling has been used widely to study various issues related to action potential propagation in cardiac tissue. In most of these previous studies, the finite difference method is used to solve the partial differential equations associated with the model. Though the finite difference approach has provided useful insight in many cases, adequate discretisation of cardiac tissue with realistic dimensions often requires a large number of nodes, making the numerical solution process difficult or impossible with available computer resources. Here, a Chebyshev pseudospectral method is presented that allows a significant reduction in the number of nodes required for a given solution accuracy. The new method is used to solve the governing nonlinear partial differential equation for the monodomain model representing a two-dimensional homogeneous sheet of cardiac tissue. The unknown transmembrane potential is expanded in terms of Chebyshev polynomial trial functions and the equation is enforced at the Gauss-Lobatto grid points. Spatial derivatives are obtained using the fast Fourier transform and the solution is advanced in time using an explicit technique. Numerical results indicate that the pseudospectral approach allows the number of nodes to be reduced by a factor of sixteen, while still maintaining the same error performance. This makes it possible to perform simulations with the same accuracy using about twelve times less CPU time and memory.

Stretch-induced changes in heart rate and rhythm: clinical observations, experiments and mathematical models

Clinical and research data indicate that active and passive changes in the mechanical environment of the heart are capable of influencing both the initiation and the spread of cardiac excitation via pathways that are intrinsic to the heart. This direction of the cross-talk between cardiac electrical and mechanical activity is referred to as mechano-electric feedback (MEF). MEF is thought to be involved in the adjustment of heart rate to changes in mechanical load and would help to explain the precise beat-to-beat regulation of cardiac performance as it occurs even in the recently transplanted (and, thus, denervated) heart. Furthermore, there is clinical evidence that MEF may be involved in mechanical initiation of arrhythmias and fibrillation, as well as in the re-setting of disturbed heart rhythm by 'mechanical' first aid procedures. This review will outline the clinical relevance of cardiac MEF, describe cellular correlates to the responses observed in situ, and discuss the role that quantitative mathematical models may play in identifying the involvement of cardiac MEF in the regulation of heart rate and rhythm.