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High-Fidelity Tetrahedral Mesh Generation from Medical Imaging Data for Fluid-Structure Interaction Analysis of Cerebral Aneurysms

Yongjie Zhang1, Wenyan Wang1, Xinghua Liang1, Yuri Bazilevs2, Ming-Chen Hsu2, Trond Kvamsdal3, Reidar Brekken4, Jørgen Isaksen5
Department of Mechanical Engineering, Carnegie Mellon University, USA
Department of Structural Engineering, University of California - San Diego, USA
Department of Applied Mathematics, SINTEF Information and Communication Technology, Trondheim, Norway
Department of Medical Technology, SINTEF Health Research, Trondheim, Norway
Department of Neurosurgery, University Hospital of North Norway, Tromsø, Norway

Computer Modeling in Engineering & Sciences 2009, 42(2), 131-150. https://doi.org/10.3970/cmes.2009.042.131

Abstract

This paper describes a comprehensive and high-fidelity finite element meshing approach for patient-specific arterial geometries from medical imaging data, with emphasis on cerebral aneurysm configurations. The meshes contain both the blood volume and solid arterial wall, and are compatible at the fluid-solid interface. There are four main stages for this meshing method: 1) Image segmentation and geometric model construction; 2) Tetrahedral mesh generation for the fluid volume using the octree-based method; 3) Mesh quality improvement stage, in which edge-contraction, pillowing, optimization, geometric flow smoothing, and mesh cutting are applied to the fluid mesh; and 4) Mesh generation for the blood vessel wall based on the boundary layer generation technique. The constructed meshes are extensively employed in a fully-coupled fluid-structure interaction analysis of vascular blood flow. This paper presents several case studies of hemodynamics in patient-specific cerebral aneurysms.

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Cite This Article

Zhang, Y., Wang, W., Liang, X., Bazilevs, Y., Hsu, M. et al. (2009). High-Fidelity Tetrahedral Mesh Generation from Medical Imaging Data for Fluid-Structure Interaction Analysis of Cerebral Aneurysms. CMES-Computer Modeling in Engineering & Sciences, 42(2), 131–150.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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