Cardiovascular disease (CVD) is a rapidly growing problem that has become responsible for the highest percentage of global deaths. The detection of CVD in patients is critical for proper treatment, with one such technique being cardiac magnetic resonance imaging (cMRI). Collected cMRI data can be used directly for the calculation of wall shear stress (WSS), which is the frictional force exerted on the wall of vessel by the blood. The WSS calculation removes the need for complex patient-specific computational modeling techniques and can be done using velocity or volumetric flowrate. WSS was found to be a factor in the correlation between blood flow and atherosclerosis. Atherosclerosis is the build-up of plaque on vessel walls and was found as an underlying condition in patients who developed CVD thus it can be used in the detection of a patient's risk of CVD. The aim of this project was to use MATLAB R2021a to optimize the computational algorithms used for the calculation of WSS directly from cMRI data, as well as to create a graphics user interface (app) for the calculation and validation of the WSS technique as a CVD detection method. Originally there were multiple MATLAB functions written and utilized in separate MATLAB scripts performing the calculations. These functions were optimized and then the MATLAB App Designer was used to create the app that utilized the optimized functions to analyze the cMRI data. The app is much easier to validate and repeat the WSS method than the previously used process. This project is significant because as this app evolves and these calculation methods are validated it will have the potential to provide an earlier less invasive CVD detection method. Providing the patients more time to make lifestyle changes, potentially having a greater impact on their health, drastically reducing their risk of CVD.
University / Institution: University of Utah
Format: In Person
SESSION C (1:45-3:15PM)
Area of Research: Engineering
Faculty Mentor: Lucas Timmins
Location: Union Building, PANORAMA EAST (1:45pm)