Blood Flow Simulation and Uncertainty Quantification in Extensive Microvascular Networks: Application to Brain Cortical Networks
Year: 2025
Type: article
Abstract: ABSTRACT Objective Microvascular blood flow simulations enhance understanding of microcirculatory phenomena at the micrometer scale by capturing heterogeneity in blood flow. However, imaged areas often only partially represent tissue regions, leading to numerous vessels crossing boundaries and strongly influencing simulated blood flows through imposed boundary conditions. Methods Two key methodological aspects of blood flow simulations are addressed: selecting appropriate boundary conditions and quantifying the inevitable impact of boundary condition uncertainties on model simulations. An adaptive method for pressure boundary conditions is proposed and rigorously evaluated in extensive brain cortical microvascular networks. The adaptive method is integrated into a Bayesian calibration framework, inferring distributions over thousands of unknown pressure boundary conditions and providing uncertainty estimates for model simulations. Results The adaptive method produces simulations consistent with reference data, yielding depth‐dependent pressure drop profiles and layer‐wise capillary blood flow profiles consistent with previous analysis. These hemodynamic phenomena generalize to biphasic blood flow simulation models incorporating in vivo viscosity formulations. Uncertainty quantification reveals a novel spatially heterogeneous and depth‐dependent pattern in blood flow uncertainty. Conclusions The adaptive method for pressure boundary conditions will be useful in future applications of both forward and inverse blood flow simulations. Uncertainty quantification complements hemodynamic predictions with associated uncertainties.
Source: Microcirculation
DOI: 10.1111/micc.70027
Author: Peter Mondrup Rasmussen
Institution: Aarhus University
Language: English
Subfield: Pulmonary and Respiratory Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $3,920
OpenAlex ID: W4414422116