The recognition of the role hemodynamic forces have in the localization and development of disease has motivated large-scale efforts to enable patient-specific simulations. When combined with computational approaches, these models can be extended to include physiologically accurate hematocrit levels in large regions of the circulatory system. Such image-based simulations can yield insight into the underlying mechanisms driving disease progression and inform surgical planning or the design of next generation drug delivery systems.
Building realistic models of transport phenomena in the human circulatory system presents a formidable mathematical and computational challenge: models must incorporate the motion of fluid, intricate geometry of the blood vessels, continual pulse-driven changes in flow and pressure, and behavior of suspended bodies such as red blood cells (RBCs). We are developing computational methods to leverage massively parallel supercomputers to address complex biomedical questions and uncover causes of disease pathologies. Working closely with physicians and experimentalists, we are establishing a tightly coupled feedback loop between the mathematical model development and in vivo and in vitro measurements. Our research aims to provide insight into the localization and development of human diseases ranging from atherosclerosis to cancer.
*Image created by Madhurima Vardhan, Duke Univeristy, and Liam Krauss, Lawrence Livermore National Laboratory.