Blood Flow Simulation and Uncertainty Quantification in Extensive Microvascular Networks: Application to Brain Cortical Networks

TY - JOUR

T1 - Blood Flow Simulation and Uncertainty Quantification in Extensive Microvascular Networks:

T2 - Application to Brain Cortical Networks

AU - Rasmussen, Peter Mondrup

N1 - Publisher Copyright: © 2025 The Author(s). Microcirculation published by John Wiley & Sons Ltd.

PY - 2025/10

Y1 - 2025/10

N2 - 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.

AB - 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.

KW - Bayesian calibration

KW - biophysical modeling

KW - blood flow

KW - boundary conditions

KW - hemodynamic simulations

KW - microcirculation

KW - microvasculature

KW - uncertainty quantification

UR - https://www.scopus.com/pages/publications/105016739278

U2 - 10.1111/micc.70027

DO - 10.1111/micc.70027

M3 - Journal article

C2 - 40975888

AN - SCOPUS:105016739278

SN - 1073-9688

VL - 32

JO - Microcirculation

JF - Microcirculation

IS - 7

M1 - e70027

ER -