Abstract

Patient-specific computational models represent a powerful tool for the planning of cardiovascular interventions. In this context, the patient-specific material properties are considered as one of the biggest source of uncertainty. In this work, we investigated the effect of the uncertainty of the elastic module (E), as computed from a recent image-based methodology, on a fluid-structure interaction (FSI) model of a patientspecific aorta. The Uncertainty Quantification (UQ) was carried out using the generalized Polynomial Chaos (gPC) method. Four deterministic simulations were run based on the four quadrature points, computed considering a deviation of ±20% on the estimation of the E value of the vessel wall from patient's imaging. The UQ of the E parameter was evaluated on the area and flow variations among cardiac cycle extracted from five cross-sections of the aortic FSI model. Results from gPC analysis showed a not significant variation of the area and flow quantities during the whole cardiac period, thus demonstrating the effectiveness of the used image-based methodology in the inferring of the E parameter, despite its intrinsic errors due to model definition. This study highlights the importance of imaging to retrieve useful data in an indirect and noninvasive way, to enhance the reliability of in-silico models in the clinical practice.