PROJECT SUMMARY
Stroke is the second leading cause of death and the third leading cause of disability worldwide. Similar trends
exist in the U.S., where it is estimated that every 40 seconds a stroke occurs, leading to a stroke-related death
every 4 minutes. Afflicting approximately 800,000 Americans each year, time sensitive and widely practicable
diagnostic algorithms are paramount. Despite increasing emphasis on the significance of blood flow
characteristics in pursuing strokes, estimating regional cerebral blood flow (rCBF) inside the skull is no trivial
task. Imaging techniques that provide rCBF distribution may be unavailable due to time constraint or access to
facilities, or simply the expenses attached to it.
In our preliminary studies, we have developed a model that can give blood flow estimates in real-time for the
entire brain vasculature. Here, we propose a novel hybrid computational model that utilizes high-performance
computing and machine learning to simulate the entire brain vasculature, as well as provide real time measures
of regional CBF. We will use novel mathematical methods to determine the vascular geometry of each subject
using our existing imaging database that includes CTA and CBF images from stroke patients, and will
subsequently validate our simulations against the same dataset. Finally, we will use machine learning algorithms
to extract patterns in stroke patients' blood flow and give estimates for stroke severity predictors on patient-
specific measurements.