Past Event: PhD Dissertation Defense

High-Speed High-Fidelity Computational Modeling Approach for Cardiac Models

Shruti Motiwale, Ph.D. Candidate, Oden Institute

Abstract

We present a high-speed, high-fidelity computational modeling approach for cardiac simulations, with a focus on replacement heart valves and left ventricular function. We have developed high-fidelity structurally-based computational models for simulating replacement heart valves. Through our structural constitutive model for electrospun biomaterials, we discovered the presence of novel fiber-fiber interactions and the absence of fiber-gel interactions in electrospun biomaterials. Moreover, we related the model parameters to structural parameters controllable in the manufacturing process, presenting a way to narrow down optimal biomaterials in-silico.  We also developed a comprehensive simulation pipeline to simulate BHV response under cycling loading upto 50 million cycles. Our model predicted lasting changes to BHV leaflet shape and underlying structure within the first 20 million cycles, and found that these changes stabilize thereafter, a crucial insight that must be incorporated during BHV design. While our models are capable of providing excellent insights, practical clinical applications of these models using traditional simulation methods are impeded due to their prohibitively slow speed. To overcome these computational challenges, we have developed a novel neural network finite element approach for high-speed high-fidelity cardiac simulations. This approach learns a family of solutions to a parametric PDE describing cardiac mechanics. The novelty of our model lies in 1) learning the underlying physics directly from the weak form of the PDE, either through the potential energy or the virtual work formulation, 2) no reliance on any additional experimental or simulation generated data for accurate predictions, 3) simultaneous training over the complete physiological loading range, 4) prediction accuracy of 0.1\% relative to the conventional finite element method along with a prediction time of a few seconds and 5) NURBS mapping integrated within the method to accurately capture complex geometry. Studies have repeatedly demonstrated the importance of a full heart model for accurate heart valve simulations, hence we have also developed a NNFE model for a heart valve leaflet and a left ventricle, serving as intermediate steps towards more sophisticated full cardiac models. Finally, we have also developed a NNFE model to simulate effects of myocardial infarction in the left ventricle. While this work is a proof-of-concept on idealized geometries, there is a clear potential to extend it to realistic shapes and simulate additional variables including variable geometry and material properties. Our approach overcomes the computational challenges in practical applications of high-fidelity cardiac computational models and opens pathways for patient-specific clinical diagnosis and treatment planning.

Biography

Shruti Motiwale is a PhD candidate in Dr. Michael Sacks' group. Her research focuses on combining finite elements with neural networks to develop a high-speed high-fidelity computational modeling approach for cardiac models. Prior to joining UT Austin, Shruti worked as a Sr. CAE engineer at Tesla. She obtained her MS in Mechanical Engineering from the Pennsylvania State University and her B.Tech in Mechanical Engineering from The Indian Institute of Technology, Bombay, India.