Bernardo Martins Rocha

Accelerating cardiac excitation spread simulations using graphics processing units

The modeling of the electrical activity of the heart is of great medical and scientific interest, because it provides a way to get a better understanding of the related biophysical phenomena, allows the development of new techniques for diagnoses and serves as a platform for drug tests. The cardiac electrophysiology may be simulated by solving a partial differential equation coupled to a system of ordinary differential equations describing the electrical behavior of the cell membrane. The numerical solution is, however, computationally demanding because of the fine temporal and spatial sampling required. The demand for real‐time high definition 3D graphics made the new graphic processing units (GPUs) a highly parallel, multithreaded, many‐core processor with tremendous computational horsepower. It makes the use of GPUs a promising alternative to simulate the electrical activity in the heart. The aim of this work is to study the performance of GPUs for solving the equations underlying the electrical activity in a simple cardiac tissue. In tests on 2D cardiac tissues with different cell models it is shown that the GPU implementation runs 20 times faster than a parallel CPU implementation running with 4 threads on a quad–core machine, parts of the code are even accelerated by a factor of 180. Copyright

Effects of deformation on transmural dispersion of repolarization using in silico models of human left ventricular wedge

Mechanical deformation affects the electrical activity of the heart through multiple feedback loops. The purpose of this work is to study the effect of deformation on transmural dispersion of repolarization and on surface electrograms using an in silico human ventricular wedge. To achieve this purpose, we developed a strongly coupled electromechanical cell model by coupling a human left ventricle electrophysiology model and an active contraction model reparameterized for human cells. This model was then embedded in tissue simulations on the basis of bidomain equations and nonlinear solid mechanics. The coupled model was used to evaluate effects of mechanical deformation on important features of repolarization and electrograms. Our results indicate an increase in the T-wave amplitude of the surface electrograms in simulations that account for the effects of cardiac deformation. This increased T-wave amplitude can be explained by changes to the coupling between neighboring myocytes, also known as electrotonic effect. The thickening of the ventricular wall during repolarization contributes to the decoupling of cells in the transmural direction, enhancing action potential heterogeneity and increasing both transmural repolarization dispersion and T-wave amplitude of surface electrograms. The simulations suggest that a considerable percentage of the T-wave amplitude (15%) may be related to cardiac deformation.

Lattice Boltzmann method for parallel simulations of cardiac electrophysiology using GPUs

This work presents the lattice Boltzmann method (LBM) for computational simulations of the cardiac electrical activity using monodomain model. An optimized implementation of the lattice Boltzmann method is presented which uses a collision model with multiple relaxation parameters in order to consider the anisotropy of the cardiac tissue. With focus on fast simulations of cardiac dynamics, due to the high level of parallelism present in the LBM, a GPU parallelization was performed and its performance was studied under regular and irregular three-dimensional domains. The results of our optimized lattice Boltzmann parallel implementation for cardiac simulations have shown acceleration factors as high as 500 × for the overall simulation and for the LBM a performance of 419 mega lattice updates per second was achieved. With near real time simulations in a single computer equipped with a modern GPU these results show that the proposed framework is a promising approach for application in a clinical workflow.

3D Heart Modeling with Cellular Automata, Mass-Spring System and CUDA

The mechanical behavior of the heart is guided by the propagation of an electrical wave, called action potential. Many diseases have multiple effects on both electrical and mechanical cardiac physiology. To support a better understanding of the multi-scale and multi-physics processes involved in physiological and pathological cardiac conditions, a lot of work has been done in developing computational tools to simulate the electro-mechanical behavior of the heart. In this work, we implemented an aplication to mimic the heart tissue behavior, based on cellular automaton, mass-spring system and parallel computing with CUDA. Our application performed 3D simulations in a very short time. In order to assess the simulation results, we compared them with another synthetic model based on well-known partial differential equationsPDE. Preliminary results suggest our application was able to reproduce the PDE results with much less computational effort.

Performance evaluation of GPU parallelization, space-time adaptive algorithms and their combination for simulating cardiac electrophysiology

The use of computer models as a tool for the study and understanding of the complex phenomena of cardiac electrophysiology has attained increased importance nowadays. At the same time, the increased complexity of the biophysical processes translates into complex computational and mathematical models. To speed up cardiac simulations and to allow more precise and realistic uses, 2 different techniques have been traditionally exploited: parallel computing and sophisticated numerical methods. In this work, we combine a modern parallel computing technique based on multicore and graphics processing units (GPUs) and a sophisticated numerical method based on a new space-time adaptive algorithm. We evaluate each technique alone and in different combinations: multicore and GPU, multicore and GPU and space adaptivity, multicore and GPU and space adaptivity and time adaptivity. All the techniques and combinations were evaluated under different scenarios: 3D simulations on slabs, 3D simulations on a ventricular mouse mesh, ie, complex geometry, sinus-rhythm, and arrhythmic conditions. Our results suggest that multicore and GPU accelerate the simulations by an approximate factor of 33×, whereas the speedups attained by the space-time adaptive algorithms were approximately 48. Nevertheless, by combining all the techniques, we obtained speedups that ranged between 165 and 498. The tested methods were able to reduce the execution time of a simulation by more than 498× for a complex cellular model in a slab geometry and by 165× in a realistic heart geometry simulating spiral waves. The proposed methods will allow faster and more realistic simulations in a feasible time with no significant loss of accuracy.

A Parallel Genetic Algorithm to Adjust a Cardiac Model Based on Cellular Automaton and Mass-Spring Systems

This work presents an electro-mechanical model of the cardiac tissue and an automatic way to tune its parameters. A cellular automaton was used to simulate the action potential propagation, and a mass-spring system to model the tissue contraction. A parallel genetic algorithm was implemented in order to automatically adjust a simple and fast discrete model, to reproduce simulations of another synthetic well known model based on differential equations DEs. Our results suggest that the discrete model was able to qualitatively reproduce the results obtained by DEs with much less computational effort.

An Electromechanical Left Ventricular Wedge Model to Study the Effects of Deformation on Repolarization during Heart Failure

Heart failure is a major and costly problem in public health, which, in certain cases, may lead to death. The failing heart undergo a series of electrical and structural changes that provide the underlying basis for disturbances like arrhythmias. Computer models of coupled electrical and mechanical activities of the heart can be used to advance our understanding of the complex feedback mechanisms involved. In this context, there is a lack of studies that consider heart failure remodeling using strongly coupled electromechanics. We present a strongly coupled electromechanical model to study the effects of deformation on a human left ventricle wedge considering normal and hypertrophic heart failure conditions. We demonstrate through a series of simulations that when a strongly coupled electromechanical model is used, deformation results in the thickening of the ventricular wall that in turn increases transmural dispersion of repolarization. These effects were analyzed in both normal and failing heart conditions. We also present transmural electrograms obtained from these simulations. Our results suggest that the waveform of electrograms, particularly the T-wave, is influenced by cardiac contraction on both normal and pathological conditions.

Simulations of Cardiac Electrophysiology Combining GPU and Adaptive Mesh Refinement Algorithms

Computer models have become valuable tools for the study and comprehension of the complex phenomena of cardiac electrophysiology. However, the high complexity of the biophysical processes translates into complex mathematical and computational models. In this paper we evaluate a hybrid multicore and graphics processing unit numerical algorithm based on mesh adaptivity and on the finite volume method to cope with the complexity and to accelerate these simulations. This is a very attractive approach since the electrical wavefront corresponds to only a small fraction of the cardiac tissue. Usually, the numerical solution of the partial differential equations that model the phenomenon requires very fine spatial discretization to follow the wavefront, which is approximately 0.2 mm. The use of uniform meshes leads to high computational cost as it requires a large number of mesh points. In this sense, the tests reported in this work show that simulations of three-dimensional models of cardiac tissue have been accelerated by more than 626 times using the adaptive mesh algorithm together with its parallelization, with no significant loss in accuracy.

Patient-Specific Left Ventricle Mesh Generation Using the Bull’s Eye of the Wall Thickness Measurements from Medical Images

This paper presents an automatic procedure for the generation of personalized left ventricle (LV) meshes based on measurements of the wall thickness in a standard format. The procedure takes as input measurements of the wall thickness (WT) of a patient and generates a computational meshes with microstructural information which is suitable for computer simulation of the electrical and mechanical cardiac activity. In this work we describe the procedure, present sample measurements from an open dataset and the generated mesh. Finally, we demonstrate the use of the created mesh in a computer simulation of the electrical activity of the LV.

An adaptive mesh algorithm for the numerical solution of electrical models of the heart

Computer models have become valuable tools for the study and comprehension of the complex phenomena of cardiac electrophysiology. However, the high complexity of the biophysical processes translates into complex mathematical and computational models. In this paper we evaluate a numerical algorithm based on mesh adaptivity and finite volume method aiming to accelerate these simulations. This is a very attractive approach since the spreading electrical wavefront corresponds only to a small fraction of the cardiac tissue. Usually, the numerical solution of the partial differential equations that model the phenomenon requires very fine spatial discretization to follow the wavefront, which is approximately 0.2 mm. The use of uniform meshes leads to high computational cost as it requires a large number of mesh points. In this sense, the tests reported in this work show that simulations of two-dimensional models of cardiac tissue have been accelerated by more than 80 times using the adaptive mesh algorithm, with no significant loss in accuracy.