Luis Paulo da Silva Barra

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.

Determination of Cardiac Ejection Fraction by Electrical Impedance Tomography - Numerical Experiments and Viability Analysis

Cardiac ejection fraction is a clinically relevant parameter that is highly correlated to the functional status of the heart. Today the non-invasive methods and technology that measure cardiac ejection fraction, such as MRI, CT and echocardiography do not offer a continuous way of monitoring this important parameter. In this work, we numerically evaluate a new method for the continuous estimation of cardiac ejection fraction based on Electrical Impedance Tomography. The proposed technique assumes the existence of recent Magnetic Resonance (MR) images of the heart to reduce the search space of the inverse problem. Simulations were performed on two-dimensional cardiac MRI images with electric potentials numerically obtained by the solution of the Poisson equation via the Boundary Element Method. Different protocols for current injection were evaluated. Preliminary results are presented and the potentialities and limitations of the proposed technique are discussed.

Multiscale modeling of the elastic moduli of lightweight aggregate concretes: numerical estimation and experimental validation

This study concerns the numerical simulation of effective elasticity modulus of lightweight concrete by means of Asymptotic Expansion Homogenization. Taking as input data the elastic pr oper ties of the aggr egate and mor tar employed in the concrete composition, it is possible to evaluate a homogenized elastic tensor for the resulting material. The present work deals with lightweight concretes made of the same mortar and five families of low density aggregates, with varying aggregate volumetric fractions. The multiscale model employed to represent the geometric and mechanical properties of the concrete is characterized by a great deal of simplicity . The validation of the adopted pr ocedur e consisted of comparisons of the numerical results with experimental measurements, showing good agreement.

Determination of Cardiac Ejection Fraction by Electrical Impedance Tomography

where PV is the volume of blood pumped, that is given by the difference between the end-diastolic volume (EDV) and the end-systolic volume (ESV). Cardiac ejection fraction is a relevant parameter because it is highly correlated to the functional status of the heart. To determine the EF, different non-invasive techniques can be used, like echocardiography, cardiac magnetic resonance and computed tomography. However, they are not suitable for continuous monitoring. In this work, we numerically evaluate a new method for the continuous estimation of cardiac ejection fraction based on Electrical Impedance Tomography (EIT).

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.

Some numerical results on the influence of measurement strategies and load patterns in the EIT inverse problem

It is well known that the Electrical Impedance Tomography reconstructed images are very sensitive to noise in the electrical potential measures at the external boundary of the domain. Furthermore, there are a variety of strategies to inject current and measure these values. Thus, the aim of this work is to verify how much some of these strategies affect the quality of the reconstructed image and the sensitivity of each strategy to the existence of noise in the measured data. For a known position and shape of a single homogeneous inclusion inside a conductor domain, measured data is numerically simulated by the forward problem solution via Boundary Element Method (BEM). Sixteen electrodes placed at the outer boundary of the domain are used with different load patterns and measurement strategies to obtain the exact data for the numerical experiments. In order to simulate noisy data, Additive White Gaussian Noise proportional to the exact measured value is used on each potential measure. The inverse problem is treated as a data fitting problem solved via Levenberg-Marquardt method, that minimizes the difference between measures data and computed potential values. The coordinates of the control points of a spline function that defines the boundary of the inclusion are used as minimization parameters. In each step of this procedure, the computed potential values are obtained via BEM. The identification of inclusions of different sizes, shapes and positions is presented allowing some conclusions about the influence of the studied parameters.

A Strategy for Parametrization Refinement in the Solution of a Geometric Inverse Problem

This paper treats the problem of identifying a single inclusion in a conductor domain based on the knowledge of measured electrical potentials on the external boundary of the conductor body due to known injected electrical currents. The inclusion boundary is approximated by an Extended X-Spline, that allows identify inclusions with smooth or sharp boundary. In this work, the forward problem is solved by an implementation of the direct formulation of the Boundary Element Method (BEM) and the inverse one is solved by Levenberg-Marquardt method, which requires the evaluation of the objective function derivatives, here approximated by finite differences. This work presents a methodology to increase the number of optimization variables during the solution of the inverse problem in order to improve the quality of the results.

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.

Artificial Neural Networks Ensemble Applied to the Electrical Impedance Tomography Problem to Determine the Cardiac Ejection Fraction

Cardiac Ejection Fraction (EF) is a parameter that indicates how much blood the heart is pumping to the body. It is a very important clinical parameter since it is highly correlated to the functional status of the heart. To measure the EF, diverse non-invasive techniques have been applied such as Magnetic Resonance. The method studied in this work is the Electrical Impedance Tomography (EIT) which consists in generate an image of the inner body using measures of electrical potentials - some electrodes are attached to the body boundary and small currents are applied in the body, the potentials are then measured in these electrodes. This technique presents lower costs and a high portability compared to others. It can be done in the patient bed and does not use ionizing radiation. The EIT problem consists in define the electrical distribution of the inner parts that results in the potentials measured. Therefore, it is considered as a non-linear inverse problem. To solve that, this work propose the application of an Artificial Neural network (ANN) Ensemble since it is simple to understand and implement. Our results show that the ANN Ensemble presents fast and good results, which are crucial for the continuous monitoring of the heart.