A fast approach to estimating Windkessel model parameters for patient-specific multi-scale CFD simulations of aortic flow

TL;DR

This paper introduces a rapid, robust method for estimating Windkessel model parameters in patient-specific aortic CFD simulations, enabling more efficient and personalized cardiovascular hemodynamics analysis.

Contribution

A novel fast approach combining geometric resistances and pattern search algorithms for patient-specific Windkessel parameter estimation in aortic flow modeling.

Findings

Method is validated with physiological and pathological cases.

Approach is computationally efficient and flexible.

Accurately captures patient-specific flow distributions.

Abstract

Hemodynamics in the aorta from computational fluid dynamics (CFD) simulations can provide a comprehensive analysis of relevant cardiovascular diseases. Coupling the three-element Windkessel model with the patient-specific CFD simulation to form a multi-scale model is a trending approach to capture more realistic flow fields. However, a set of parameters (e.g., R_c, R_p, and C) for the Windkessel model need to be tuned case by case to reflect patient-specific flow conditions. In this study, we propose a fast approach to estimating these parameters under both physiological and pathological conditions. The approach consists of the following steps: (1) finding geometric resistances for each branch using a steady CFD simulation; (2) using the pattern search algorithm to search the parameter spaces by solving the flow circuit system with the consideration of geometric resistances; (3) performing the multi-scale modeling of aortic flow with the optimized Windkessel model parameters. The method was validated through a series of numerical experiments to show its flexibility and robustness, including physiological and pathological flow distributions at each downstream branch from a healthy aortic geometry or a stenosed geometry. This study demonstrates a flexible and computationally efficient way to capture patient-specific hemodynamics in the aorta, facilitating the personalized biomechanical analysis of aortic flow.