Determination of vessel wall shear stress in the cerebral vasculature using magnetic resonance imaging and computational fluid dynamics
Our project consists of a combination of magnetic resonance imaging (MRI) and numerical simulations to model the blood flow dynamics in major cerebral arteries. In doing so, we hope to determine the shear stresses that are inflicted on the vessel walls of the brain, which should correlate with the vessel’s ability to dilate in response to a vasoactive stimulus. This autoregulatory effect can be measured in terms of cerebrovascular reactivity and is shown to be a useful biomarker for cerebral disease.
The experiment protocol consists of a series of MRI scans to determine the flow velocity and spatial-temporal profile at target locations within the circle of Willis, which connects all the major supply arteries of the brain. Data from these scans will be imported into a simulation program that utilizes computational fluid dynamics (CFD) to predict blood flow properties for the entire circulatory system in the brain.
The selected student will primarily be responsible for developing a protocol for translating the MR data acquired from healthy volunteers into input values that can be accepted into the CFD simulation. Specific duties include, but are not limited to, vessel segmentation and edge detection, noise error simulation, source code debugging, and anatomical and flow data modeling. Literature research will also be an important component of the project as the student will need to be knowledgeable of past and current publications on this subject.