CAREER: GPU-Accelerated Framework for Integrated Modeling and Biomechanics Simulations of Cardiac Systems

Iowa State University, Ames IA

Investigators

Abstract

Cardiovascular diseases, such as heart failure, are one of the leading cause of death in the U.S. and pose a severe burden to the healthcare system. Most current treatments for cardiovascular diseases are based on rough estimates of outcomes from the results of clinical trials, which might not apply to individual patients due to patient-specific variations. Computational models of the cardiovascular system, developed from patient-specific clinical data, can help refine the diagnosis and personalize the treatment, significantly improving patient care and reducing mortality. The current patient-specific methods for cardiovascular diseases have been demonstrated mainly in simple, isolated examples. For widespread adoption of personalized medicine, a flexible and easy-to-use framework for integrating patient data and simulating cardiac biomechanics needs to be developed. This project focuses on creating an integrative framework with simulation, analysis, and visualization tools that will significantly advance the state-of-the-art in personalized medicine, ultimately improving patient care and treatment outcomes. Results from this research will benefit the U.S. healthcare system, society, and economy, while supporting the NSF mission to promote the progress of science and advance the national health. The tools developed as a part of this research involves several disciplines including computer science, bioengineering, and mechanical engineering. The multidisciplinary components of the project is being integrated into a larger educational effort that offers the students a solid foundation in developing computational tools and algorithms, while also broadening the participation of underrepresented groups in research. The primary objective of this research is the advancement of the state-of-the-art in translational medicine with the help of computational modeling and interactive analysis tools to improve the basic understanding of the cardiac muscle and personalize treatment of cardiovascular diseases in patients. The research focuses on creating a novel computational framework to automate biomechanics finite-element simulation and analysis of patient-specific cardiac systems. Further, it aims to advance the knowledge of disease and therapeutic mechanisms by developing advanced multiscale methods to model muscle contraction and growth. Some of the key computational tools and methods proposed as part of this framework include: (1) a geometric mesh generation tool for systematic generation of patient-specific finite element meshes from clinical data; (2) an algorithm for accelerating high-order finite-element simulations using the graphics processing unit (GPU) for fast tuning of model parameters to match the patients' baseline cardiac function; (3) new methods for multiphysics simulations of cardiac systems to model multi-scale muscle mechanics and tissue growth; and (4) new visualization and virtual reality tools to enable animated volume rendering and visual analytics of the results of the cardiac simulations. Successful development of these open-source tools will enable faster adoption of patient-specific computational models by the research community to understand therapeutic mechanisms. This framework can significantly advance the state-of-the-art in personalized medicine, ultimately improving patient care and treatment outcomes. The multidisciplinary components of the project is being integrated into a larger educational effort to offer students a solid foundation in combining biomedical engineering with scientific computing. The education and outreach plans of this research can inform the community about the crucial role of computational models in improving patient-care. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.