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...
| Autores: | , , , |
|---|---|
| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2013 |
| País: | Argentina |
| Institución: | Consejo Nacional de Investigaciones Científicas y Técnicas |
| Repositorio: | CONICET Digital (CONICET) |
| Idioma: | inglés |
| OAI Identifier: | oai:ri.conicet.gov.ar:11336/3924 |
| Acceso en línea: | http://hdl.handle.net/11336/3924 |
| Access Level: | acceso abierto |
| Palabra clave: | Cerebral aneurysms Medical Image Processing 4D Computerized Tomographic Angiography Wall Motion Wall Shear Stress https://purl.org/becyt/ford/3.2 https://purl.org/becyt/ford/3 |
| Sumario: | 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. |
|---|