A surrogate model for plaque modeling in carotids based on Robin conditions calibrated by cine MRI data. (25th February 2021) - Pozzi, Silvia; Domanin, Maurizio; Forzenigo, Laura; Votta, Emiliano; Zunino, Paolo; Redaelli, Alberto; Vergara, Christian
Abstract: We propose a surrogate model for the fluid–structure interaction (FSI) problem for the study of blood dynamics in carotid arteries in presence of plaque. This is based on the integration of a numerical model with subject‐specific data and clinical imaging. We propose to model the plaque as part of the tissues surrounding the vessel wall through the application of an elastic support boundary condition. In order to characterize the plaque and other surrounding tissues, such as the close‐by jugular vein, the elastic parameters of the boundary condition were spatially differentiated and their values were estimated by minimizing the discrepancies between computed vessel displacements and reference values obtained from CINE Magnetic Resonance Imaging data. We applied the model to three subjects with a degree of stenosis greater than 70%. We found that accounting for both plaque and jugular vein in the estimation of the elastic parameters increases the accuracy. In particular, in all patients, mismatches between computed and in vivo measured wall displacements were one to two orders of magnitude lower than the spatial resolution of the original MRI data. These results confirmed the validity of the proposed surrogate plaque model. We also compared fluid‐dynamics results with those obtained in a fixed wall setting and in a full FSI model, used as gold standard, highlighting the better accordance of our results in comparison to the rigid ones. Abstract : We propose a surrogate model for the description of plaque in carotid arteries in FSI problems. The plaque is modeled as part of the tissues surrounding the vessel wall through the application of an elastic support boundary condition. The values of the parameters in such conditions were estimated by means of CINE MRI measures. We successfully applied our strategy to three subjects with a degree of stenosis greater than 70% and compare the results with a full FSI model.