# Modeling tissue perfusion in terms of 1d-3d embedded mixed-dimension coupled problems with distributed sources

## TL;DR

This paper introduces a novel mixed-dimension modeling approach for tissue perfusion that improves accuracy and convergence in simulating fluid exchange between microvasculature and surrounding tissue, enabling larger-scale simulations.

## Contribution

The method models vascular networks with embedded 1D segments and distributed sources, enhancing flux approximation and convergence over existing mixed-dimension techniques.

## Key findings

- Error in exchange flux approximation reduced by a factor of 3 for coarse grids.
- Improved convergence rates due to increased regularity of pressure solutions.
- Potential for larger domain simulations with limited computational resources.

## Abstract

We present a new method for modeling tissue perfusion on the capillary scale. The microvasculature is represented by a network of one-dimensional vessel segments embedded in the extra-vascular space. Vascular and extra-vascular space exchange fluid over the vessel walls. This exchange is modeled by distributed sources using smooth kernel functions for the extra-vascular domain. It is shown that the proposed method may significantly improve the approximation of the exchange flux, in comparison with existing methods for mixed-dimension embedded problems. Furthermore, the method exhibits better convergence rates of the relevant quantities due to the increased regularity of the extra-vascular pressure solution. Numerical experiments with a vascular network from the rat cortex show that the error in the approximation of the exchange flux for coarse grid resolution may be decreased by a factor of $3$. This may open the way for computing on larger network domains, where a fine grid resolution cannot be achieved in practical simulations due to constraints in computational resources, for example in the context of uncertainty quantification.