Student Posters Repository

Using CT and MRI guided computational fluid dynamics (CFD) to simulate blood flow in patient-specific geometries is a growing field that is generating results on the order of terabytes. To effectively use this data in clinical decision making, physicians must know what computed values from simulations are most important in determining the severity of a patient’s condition. Here, we study the risk of varying degrees of aortic coarctation (congenital narrowing of the aorta). One risk is atherosclerotic progression, which has been shown to correlate with low wall shear stress. We study how the severity of the coarctation, in terms of downstream wall shear stress (WSS), is affected by gross morphological characteristics, patient attributes, and other comorbidities. Hemodynamic simulations of the aortic coarctation geometries were performed using HARVEY, a massively parallel hemodynamics package developed by Randles et al. We show that by choosing and developing physiologically relevant features, such as the variance of WSS within the coarctation, we are able to perform unsupervised classification of our simulated patient data. Our tools allow us to probe which computed results are important in predicting WSS and other hemodynamic variables.