Quantitative Evaluation of Patient-Specific Conforming Hexahedral Meshes of Abdominal Aortic Aneurysms and Intraluminal Thrombus Generated from MRI

Abstract

Abstract-A novel method for generating patient-specific high quality conforming hexahedral meshes is presented. The meshes are directly obtained from the segmentation of patient magnetic resonance (MR) images of abdominal aortic aneurysms (AAA). The MRI permits distinguishing between structures of interest in soft tissue. Being so, the contours of the lumen, the aortic wall and the intraluminal thrombus (ILT) are available and thus the meshes represent the actual anatomy of the patient's aneurysm, including the layered morphologies of these structures. Most AAAs are located in the lower part of the aorta and the upper section of the iliac arteries, where the inherent tortuosity of the anatomy and the presence of the ILT makes the generation of high-quality elements at the bifurcation is a challenging task. In this work we propose a novel approach for building quadrilateral meshes for each surface of the sectioned geometry, and generating conforming hexahedral meshes by combining the quadrilateral meshes. Conforming hexahedral meshes are created for the wall and the ILT. The resulting elements are evaluated on four patients' datasets using the Scaled Jacobian metric. Hexahedral meshes of 25,000 elements with 94.8% of elements well-suited for FE analysis are generated.

Generación de modelos físicos personalizados y segmentación de la pared arterial y el trombo intraluminal en imágenes de aneurismas aórticos abdominales

“…En este capítulo se presenta un método novedoso y robusto para generar mallas hexaédricas tanto de la pared aórtica como del trombo, manteniendo sus propiedades de adyacencia, utilizando elementos de alta calidad, prestando especial atención al modelado de la bifurcación, y a que sean adecuadas para el análisis de FE. El método tiene en cuenta la evolución de la línea central del vaso en el espacio tridimensional y genera la malla directamente a partir de las imágenes segmentadas, sin necesidad de reconstruir superficies triangulares (Tarjuelo-Gutierrez et al ,2012;Tarjuelo-Gutierrez et al ,2013;Tarjuelo-Gutierrez et al ,2014).…”