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Erschienen in: Medical & Biological Engineering & Computing 6/2010

01.06.2010 | Original Article

Computation of a finite element-conformal tetrahedral mesh approximation for simulated soft tissue deformation using a deformable surface model

verfasst von: Frank Weichert, Andreas Schröder, Constantin Landes, Ali Shamaa, Said Kamel Awad, Lars Walczak, Heinrich Müller, Mathias Wagner

Erschienen in: Medical & Biological Engineering & Computing | Ausgabe 6/2010

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Abstract

In this article, we present a new method for the generation of surface meshes of biological soft tissue. The method is based on the deformable surface model technique and is extended to histological data sets. It relies on an iterative adjustment towards polygonal segments describing the histological structures of the soft tissue. The generated surface meshes allow for the construction of volumetric meshes through a standard constrained Delaunay approach and, thus, for the application in finite element methods. The geometric properties of volumetric meshes have an immediate influence on the numerical conditioning and, therewith, on the stability of the finite element method and the convergence of iterative solvers. In this article, the influence of the surface meshes on the quality of the volumetric meshes is analysed in terms of the spectral condition number of the stiffness matrices, which are assembled within Newton’s method. The non-linear material behavior of biological soft tissue is modeled by the Mooney–Rivlin material law. The subject is motivated by the requirements of virtual surgery.

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Metadaten
Titel
Computation of a finite element-conformal tetrahedral mesh approximation for simulated soft tissue deformation using a deformable surface model
verfasst von
Frank Weichert
Andreas Schröder
Constantin Landes
Ali Shamaa
Said Kamel Awad
Lars Walczak
Heinrich Müller
Mathias Wagner
Publikationsdatum
01.06.2010
Verlag
Springer-Verlag
Erschienen in
Medical & Biological Engineering & Computing / Ausgabe 6/2010
Print ISSN: 0140-0118
Elektronische ISSN: 1741-0444
DOI
https://doi.org/10.1007/s11517-010-0607-0

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