Speaker
Description
Vascular stenosis, the pathological narrowing of blood vessels due to atherosclerotic plaque, remains a primary driver of cardiovascular disease. Traditional computational fluid dynamics (CFD) models provide detailed hemodynamics but are computationally expensive, limiting their utility for long-term disease forecasting. To address this, we propose a highly efficient stochastic modeling framework designed to simulate both the formation and treatment processes of vascular stenosis. Instead of relying on complex, deterministic fluid calculations, our approach utilizes a probabilistic model based on discrete particle movement. The dynamics of these particles are governed by a combination of diffusion processes and an approximated flow field derived from the Stokes equations. This simplified structure successfully captures the essential macroscopic fluid-structure interactions and the microscopic probabilistic fluctuations of plaque growth. By significantly reducing computational loads without losing critical physical behaviors, this methodology offers a flexible and scalable platform for evaluating long-term disease progression and potential therapeutic interventions.
