Three-dimensional (3-D) medical imaging creates notable opportunities as input toward engineering analyses, whether for basic understanding of the normal function or patho-physiology of an organ, or for the simulation of virtual surgical procedures. These analyses most often require finite element (FE) models to be constructed from patient-specific 3-D medical images. However, creation of such models can be extremely labor-intensive; in addition, image processing and mesh generation are often operator-dependent, lack robustness and may be of suboptimal quality. Focusing on the human aorta, the goal of the present work is to create a fast and robust methodology for quadrilateral surface and hexahedral volume meshing from 3-D medical images with minimal user input. By making use of the segmentation capabilities of the 3-D gradient vector flow field combined with original ray-tracing and orientation control algorithms, we will demonstrate that it is possible to incrementally grow a structured quadrilateral surface mesh of the inner wall of the aorta. The process does not only require minimal input from the user, it is also robust and very fast compared to existing methods; it effectively combines segmentation and meshing into one single effort. After successfully testing the methodology and measuring the quality of the meshes produced by it from synthetic as well as real medical image datasets, we will make use of the surface mesh of the inner aortic wall to derive hexahedral meshes of the aortic wall thickness and of the fluid domain inside the aorta. We will finally outline a tentative approach to merge several structured meshes to process the main branches of the aorta.

Three-dimensional (3-D) medical imaging creates notable opportunities as input toward engineering analyses, whether for basic understanding of the normal function or patho-physiology of an organ, or for the simulation of virtual surgical procedures. These analyses most often require finite element (FE) models to be constructed from patient-specific 3-D medical images. However, creation of such models can be extremely labor-intensive; in addition, image processing and mesh generation are often operator-dependent, lack robustness and may be of suboptimal quality. Focusing on the human aorta, the goal of the present work is to create a fast and robust methodology for quadrilateral surface and hexahedral volume meshing from 3-D medical images with minimal user input. By making use of the segmentation capabilities of the 3-D gradient vector flow field combined with original ray-tracing and orientation control algorithms, we will demonstrate that it is possible to incrementally grow a structured quadrilateral surface mesh of the inner wall of the aorta. The process does not only require minimal input from the user, it is also robust and very fast compared to existing methods; it effectively combines segmentation and meshing into one single effort. After successfully testing the methodology and measuring the quality of the meshes produced by it from synthetic as well as real medical image datasets, we will make use of the surface mesh of the inner aortic wall to derive hexahedral meshes of the aortic wall thickness and of the fluid domain inside the aorta. We will finally outline a tentative approach to merge several structured meshes to process the main branches of the aorta.

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