Key result
Hybrid discrete-continuum model effectively predicts microvascular blood pressure despite incomplete capillary architectural data.
Why the study?
Blood pressure prediction in large microvascular tissues is challenging when capillary network architecture is unknown, requiring effective modeling approaches.
A novel 3D discrete-continuum model provides a computationally feasible method for predicting blood pressure in microvascular networks with poorly defined capillary architecture.
May aid microvascular BP modeling with sparse data; leaves open clinical validation before any practice use.
We present a 3D discrete-continuum model to simulate blood pressure in large microvascular tissues in the absence of known capillary network architecture. Our hybrid approach combines a 1D Poiseuille flow description for large, discrete arteriolar and venular networks coupled to a continuum-based Darcy model, point sources of flux, for transport in the capillary bed. We evaluate our hybrid approach using a vascular network imaged from the mouse brain medulla/pons using multi-fluorescence high-resolution episcopic microscopy (MF-HREM). We use the fully-resolved vascular network to predict the hydraulic conductivity of the capillary network and generate a fully-discrete pressure solution to benchmark against. Our results demonstrate that the discrete-continuum methodology is a computationally feasible and effective tool for predicting blood pressure in real-world microvascular tissues when capillary microvessels are poorly defined.
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Sweeney et al. (2022) studied Microvascular blood flow. Hybrid discrete-continuum model vs. Fully-discrete pressure solution was evaluated on Blood pressure prediction. The hybrid discrete-continuum model effectively predicted blood pressure in microvascular tissues with incomplete capillary architectural information.
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