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

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|September 21, 2025
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Summary
This summary is machine-generated.

An adaptive method improves microvascular blood flow simulations by addressing boundary condition uncertainties. This approach provides accurate hemodynamic predictions and quantifies blood flow uncertainty in complex networks.

Keywords:
Bayesian calibrationbiophysical modelingblood flowboundary conditionshemodynamic simulationsmicrocirculationmicrovasculatureuncertainty quantification

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Area of Science:

  • Physiology
  • Biophysics
  • Computational Biology

Background:

  • Microvascular blood flow simulations are crucial for understanding microcirculation.
  • Imaged areas in simulations often represent only partial tissue regions, leading to boundary condition challenges.

Purpose of the Study:

  • To develop and evaluate an adaptive method for pressure boundary conditions in microvascular blood flow simulations.
  • To quantify the impact of boundary condition uncertainties on simulation outcomes.

Main Methods:

  • An adaptive method for pressure boundary conditions was proposed and evaluated.
  • The method was integrated into a Bayesian calibration framework for uncertainty quantification.
  • Simulations were performed on extensive brain cortical microvascular networks.

Main Results:

  • The adaptive method yielded simulations consistent with reference data.
  • Depth-dependent pressure drop and layer-wise capillary blood flow profiles were accurately reproduced.
  • Uncertainty quantification revealed spatially heterogeneous and depth-dependent blood flow uncertainty patterns.

Conclusions:

  • The adaptive method enhances microvascular blood flow simulations, applicable to both forward and inverse problems.
  • Uncertainty quantification provides crucial context for hemodynamic predictions.