Estimation of Aneurysm Wall Motion from 4D Computerized Tomographic Angiography Images

It is widely accepted that wall shear stressis associated to aneurysm formation, growthand rupture. Early identification of potential risk factors may contribute to decide the treatment and improve patient care. Previous studies have shown associations between high aneurysm wall shear stress values and both elevated risk of rupture and localization of regions of aneurysm progression. Based on the assumption that damaged regions of the endothelium have different mechanical properties, regions with differentiated wall displacement amplitudes are expected. A previous approach based on the analysis ofbidimensional dynamic tomographic angiography images at a limited number of points during the cardiac cycle showed only small displacements in some patients using that simplified and semi-automatic low resolution methodology. The purpose of this work is to overcome some of those limitations. High time and spatial resolution four dimensional computerized tomographic angiography images of cerebral aneurysms were acquired and analyzed in order to identify and characterize wall motion. Images were filtered andsegmented at nineteentime points during the cardiac cycle.An average image was computed to generate the vascular model. Anunstructured mesh of tetrahedral elements was generated using an advancing front technique. A finite element blood flow simulationwas carried out under personalized pulsatile flow conditions. A fuzzy c-means clustering algorithm was used to estimate regions that exhibit wall motion within the aneurysm sac. A good correlation between localization of regions of elevated wall shear stress and regionsexhibiting wall motion was found.Fil: Castro, Marcelo Adrian. Universidad Tecnológica Nacional. Facultad Regional Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Ahumada Olivares, María C.. Universidad Favaloro; ArgentinaFil: Putman, Christopher M. . Inova Fairfax Hospital. Department of Interventional Neuroradiology; Estados UnidosFil: Cebral, Juan R.. George Mason University. Department of Computational and Data Sciences; Estados Unido

Unsteady wall shear stress analysis from image-based computational fluid dynamic aneurysm models under Newtonian and Casson rheological models

The aim of this work was to determine whether or not Newtonian rheology assumption in image-based patient-specific computational fluid dynamics (CFD) cerebrovascular models harboring cerebral aneurysms may affect the hemodynamics characteristics, which have been previously associated with aneurysm progression and rupture. Ten patients with cerebral aneurysms with lobulations were considered. CFD models were reconstructed from 3DRA and 4DCTA images by means of region growing, deformable models, and an advancing front technique. Patient-specific FEM blood flow simulations were performed under Newtonian and Casson rheological models. Wall shear stress (WSS) maps were created and distributions were compared at the end diastole. Regions of lower WSS (lobulation) and higher WSS (neck) were identified. WSS changes in time were analyzed. Maximum, minimum and time-averaged values were calculated and statistically compared. WSS characterization remained unchanged. At high WSS regions, Casson rheology systematically produced higher WSS minimum, maximum and time-averaged values. However, those differences were not statistically significant. At low WSS regions, when averaging over all cases, the Casson model produced higher stresses, although in some cases the Newtonian model did. However, those differences were not significant either. There is no evidence that Newtonian model overestimates WSS. Differences are not statistically significant.Fil: Castro, Marcelo Adrian. Universidad Tecnológica Nacional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Olivares, María C. Ahumada. Universidad Favaloro; ArgentinaFil: Putman, Christopher M.. Texas Neurointerventional Surgery Associates; Estados UnidosFil: Cebral, Juan R.. George Mason University; Estados Unido