Computational Methods for Cardiovascular Modeling
Patient specific models are typically constructed from medical image data, allowing for a customized 3D anatomic model for individual patients. However, current image segmentation methods are often laborious and time-consuming. We are developing machine learning methods to accelerate the image segmentation process for cardiovascular patient-specific model construction.
Funding: NSF SSI
Fluid Structure Interaction
Fluid structure interaction is needed in problems requiring simultaneous solution of fluid and solid mechanics for problems with moving boundaries. We are developing tools to handle wall motion in vascular, ventricular and heart valve simulations.
Funding: Stanford Children's Health Research Institute, NSF CAREER
Vascular Growth and Remodeling
Veins and arteries are known to adapt and remodeling in response to changing hemodynamics and mechanical forces. We are adapting arterial growth and remodeling models for use in veins to uncover causes of vein graft failure. We are also developing novel unified finite element methods capable of handling realistic incompressible biological tissues. This will enable fluid solid growth simulations capturing both fluid structure interaction and long-term vascular remodeling.
FUNDING: NIH NHLBI
Patient-specific simulations must be coupled to reduced order models to capture the dynamic interplay between local hemodynamics and circulatory physiology. We have developed efficient coupling methods to link simulations with lumped parameter networks describing the closed-loop circulatory system. We are also developing parameter estimation methods for automated tuning of modeling parameters to match clinical data.
Funding: Leducq Foundation, NSF CDSE, NIH NHLBI, NIH NIBIB
We lead the development of the SimVacsular Open Source project. SimVacsular is the only open source software providing a complete pipeline from medical image data segmentation to patient specific blood flow simulation and analysis. The source code can be found on GitHub and more information, including documentation and tutorials can be found at the SimVascular website.