Abstract

We wish to simulate blood flow in human arterial and venous networks. In order to do this, we require a mathematical model to solve and a computational domain in which to solve the equations. Such models can be useful tools for gaining deeper insight into pathological conditions, such as coronary stenosis, and predicting the impact of pathologies on the tissues supplied with blood by the impacted blood vessels. Here, I discuss the process of generating 1D representations of network from medical images to use as computational domains. Essentially, we seek to create a directed tree from the imaged data in which the vertices have attributes of position in R^3 and a radius value and the edges have attributes of arc-length. From such a graph, we can prune edges in a trade-off between computational domain fidelity and computational time. From the combination of the final tree and pruned data we are able to estimate the region of an organ that is perfused from a given vessel. Finally, we will see how the structure of the computational domain impacts simulated 1D flow in the network.