Vijay Vedula

Computational modeling of cardiac hemodynamics

The proliferation of four-dimensional imaging technologies, increasing computational speeds, improved simulation algorithms, and the widespread availability of powerful computing platforms is enabling simulations of cardiac hemodynamics with unprecedented speed and fidelity. Since cardiovascular disease is intimately linked to cardiovascular hemodynamics, accurate assessment of the patients hemodynamic state is critical for the diagnosis and treatment of heart disease. Unfortunately, while a variety of invasive and non-invasive approaches for measuring cardiac hemodynamics are in widespread use, they still only provide an incomplete picture of the hemodynamic state of a patient. In this context, computational modeling of cardiac hemodynamics presents as a powerful non-invasive modality that can fill this information gap, and significantly impact the diagnosis as well as the treatment of cardiac disease. This article reviews the current status of this field as well as the emerging trends and challenges in cardiovascular health, computing, modeling and simulation and that are expected to play a key role in its future development. Some recent advances in modeling and simulations of cardiac flow are described by using examples from our own work as well as the research of other groups.

Effect of the mitral valve on diastolic flow patterns

The leaflets of the mitral valve interact with the mitral jet and significantly impact diastolic flow patterns, but the effect of mitral valve morphology and kinematics on diastolic flow and its implications for left ventricular function have not been clearly delineated. In the present study, we employ computational hemodynamic simulations to understand the effect of mitral valve leaflets on diastolic flow. A computational model of the left ventricle is constructed based on a high-resolution contrast computed-tomography scan, and a physiological inspired model of the mitral valve leaflets is synthesized from morphological and echocardiographic data. Simulations are performed with a diode type valve model as well as the physiological mitral valve model in order to delineate the effect of mitral-valve leaflets on the intraventricular flow. The study suggests that a normal physiological mitral valve promotes the formation of a circulatory (or “looped”) flow pattern in the ventricle. The mitral valve leaflets also increase the strength of the apical flow, thereby enhancing apical washout and mixing of ventricular blood. The implications of these findings on ventricular function as well as ventricular flow models are discussed.

Computational Study of the Dynamics of a Bileaflet Mechanical Heart Valve in the Mitral Position

A computational study of the flow-structure interaction of a bileaflet mechanical heart valve in the mitral position is presented. Flow in a simple model of the left ventricle is simulated using an immersed boundary method, and the dynamics of the valve leaflets are solved in a fully-coupled manner with the flow. Simulations are conducted for two distinct valve orientations and multiple valve hinge locations, and the performance of the valve is compared in terms of metrics associated with leaflet motion, mitral regurgitation, and mechanical energy losses through the valve. Results indicate that a bileaflet mechanical heart valve with a more centrally located hinge, and implanted in the anatomical orientation provides the best overall performance. The fluid and leaflet dynamics, as well as the clinical implications underlying these findings are discussed.

Hemodynamics in the Left Atrium and Its Effect on Ventricular Flow Patterns

In the present study, we investigate the hemodynamics inside left atrium (LA) and understand its impact on the development of ventricular flow patterns. We construct the heart model using dynamic-computed tomographic images and perform simulations using an immersed boundary method based flow solver. We show that the atrial hemodynamics is characterized by a circulatory flow generated by the left pulmonary veins (LPVs) and a direct stream from the right pulmonary veins (RPVs). The complex interaction of the vortex rings formed from each of the PVs leads to vortex breakup and annihilation, thereby producing a regularized flow at the mitral annulus. A comparison of the ventricular flow velocities between the physiological and a simplified pipe-based atrium model shows that the overall differences are limited to about 10% of the peak mitral flow velocity. The implications of this finding on the functional morphology of the left heart as well the computational and experimental modeling of ventricular hemodynamics are discussed.

A New MRI-Based Model of Heart Function with Coupled Hemodynamics and Application to Normal and Diseased Canine Left Ventricles

A methodology for the simulation of heart function that combines an MRI-based model of cardiac electromechanics (CE) with a Navier–Stokes-based hemodynamics model is presented. The CE model consists of two coupled components that simulate the electrical and the mechanical functions of the heart. Accurate representations of ventricular geometry and fiber orientations are constructed from the structural magnetic resonance and the diffusion tensor MR images, respectively. The deformation of the ventricle obtained from the electromechanical model serves as input to the hemodynamics model in this one-way coupled approach via imposed kinematic wall velocity boundary conditions and at the same time, governs the blood flow into and out of the ventricular volume. The time-dependent endocardial surfaces are registered using a diffeomorphic mapping algorithm, while the intraventricular blood flow patterns are simulated using a sharp-interface immersed boundary method-based flow solver. The utility of the combined heart-function model is demonstrated by comparing the hemodynamic characteristics of a normal canine heart beating in sinus rhythm against that of the dyssynchronously beating failing heart. We also discuss the potential of coupled CE and hemodynamics models for various clinical applications.