Uncertainty quantification in medical image-based hemodynamic computations

Abstract

In this paper, we present a framework for uncertainty quantification in medical image-based patient-specific hemodynamic computations. To illustrate the overall methodology, we have used an aortic coarctation model for computing trans-stenotic pressure gradient. Variance-based Sobol sensitivity indices are used to evaluate the relative influence of the various uncertain measurements and model parameters on the global variance of the output. Next, a generalized Polynomial Chaos Expansion (PCE) method is used to quantify the uncertainties in the computed mean and peak pressure gradient in terms of a probability density functions and error bars over a full cardiac cycle.