The investigators will research and develop parallel dynamic meshing algorithms, theory, and software for use in the patient-specific design of inferior vena cava (IVC) filters for improved treatment of deep vein thrombosis (DVT). This life-threatening disease results when blood clots form in a deep vein; DVT effects over 2,000,000 people in the U.S. each year. A mechanical filter is often placed in the IVC in order to trap a moving blood clot before it reaches the heart. A typical doctor uses only one or two types of filters when treating his patients rather than selecting the most appropriate IVC filter for his patient's condition. Current computational simulations of blood clot entrapment by IVC filters are of low accuracy. Hence, techniques to generate accurate meshes of the IVC and surrounding veins, IVC filter, and blood clots for use in such simulations will be developed.
The investigators will develop novel, parallel dynamic meshing techniques to generate accurate meshes of the IVC and surrounding veins, IVC filter, and blood clots. The investigators will develop parallel mesh warping algorithms and muticore software for updating the meshes in response to patient-specific deformations. Parallel geometric and topological mesh optimization methods in order to improve the quality of the meshes will be developed. The algorithms will be encapsulated in the form of a parallel dynamic meshing toolkit for simulation-assisted medical interventions. In addition, the researchers will develop a theoretical framework for dynamic meshing for improved quantitative understanding of deformations. Educational activities at the college- and pre-college level will be designed to build pathways for women and underrepresented students to pursue computational science.
