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Unstructured mesh generation from the Virtual Family models for whole body biomedical simulations
Author(s) -
Dominik Szczerba,
Esra Neufeld,
Marcel Zefferer,
Gábor Székely,
Niels Kuster
Publication year - 2010
Publication title -
procedia computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.334
H-Index - 76
ISSN - 1877-0509
DOI - 10.1016/j.procs.2010.04.091
Subject(s) - computer science , polygon mesh , mesh generation , finite element method , domain (mathematical analysis) , computational science , distributed computing , theoretical computer science , computer engineering , computer graphics (images) , mathematical analysis , physics , mathematics , thermodynamics
Physiological systems are inherently complex, involving multi-physics phenomena at a multitude of spatial and temporal scales. To realistically simulate their functions, detailed high quality multi-resolution often patient specific human models are required. Mesh generation has remained a central topic in finite element analysis (FEA) for a few decades now. Recent developments in high performance computing (HPC) driven by the need for multi-physics multiscale simulations of physiological systems define new challenges in this area. Even though many algorithms have been developed over years and are offered as commercial packages, they are often limited to mechanical engineering applications only. Mesh generation for human anatomical domains requires more effective and flexible techniques to tackle their greater geometrical and topological complexities. We present, evaluate and discuss several methods to generate unstructured body fitted multi-domain adaptive meshes with geometrically and topologically compatible interfaces from the segmented cross-sections of the Virtual Family models for the purpose of large scale whole body simulations. We found that an automated solution is difficult to achieve with real-image qualities, but if optimal methods are selected, good results can be achieved with minimal user-interactions. Therefore we believe that our observations can serve as guidance when choosing an optimal method for a specific application

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