gms | German Medical Science

55. Jahrestagung der Deutschen Gesellschaft für Neurochirurgie e. V. (DGNC)
1. Joint Meeting mit der Ungarischen Gesellschaft für Neurochirurgie

Deutsche Gesellschaft für Neurochirurgie (DGNC) e. V.

25. bis 28.04.2004, Köln

Improved Finite-Element mesh generation of the skullbase using a novel algorithm

Verbesserte Finite-Elemente-Netzgenerierung der Schädelbasis unter Verwendung eines neuartigen Algorithmus

Meeting Abstract

  • corresponding author Jan Kaminsky - Abteilung Neurochirurgie, Medizinische Hochschule Hannover, Hannover
  • T. Rodt - Abteilung Neurochirurgie, Medizinische Hochschule Hannover, Hannover
  • F. Donnerstag - Abteilung Neuroradiologie, Medizinische Hochschule Hannover, Hannover
  • M. Zumkeller - Abteilung Neuroradiologie, Medizinische Hochschule Hannover, Hannover

Deutsche Gesellschaft für Neurochirurgie. Ungarische Gesellschaft für Neurochirurgie. 55. Jahrestagung der Deutschen Gesellschaft für Neurochirurgie e.V. (DGNC), 1. Joint Meeting mit der Ungarischen Gesellschaft für Neurochirurgie. Köln, 25.-28.04.2004. Düsseldorf, Köln: German Medical Science; 2004. DocMO.11.07

Die elektronische Version dieses Artikels ist vollständig und ist verfügbar unter: http://www.egms.de/de/meetings/dgnc2004/04dgnc0113.shtml

Veröffentlicht: 23. April 2004

© 2004 Kaminsky et al.
Dieser Artikel ist ein Open Access-Artikel und steht unter den Creative Commons Lizenzbedingungen (http://creativecommons.org/licenses/by-nc-nd/3.0/deed.de). Er darf vervielf&aauml;ltigt, verbreitet und &oauml;ffentlich zug&aauml;nglich gemacht werden, vorausgesetzt dass Autor und Quelle genannt werden.


Gliederung

Text

Objective

The current imaging technologies provide high-resolution data that allows biomechanic analysis for medical purposes using the Finite-Element modeling technique. However, Finite-Element modeling of complex anatomical structures is not solved satisfyingly so far as mesh quality and time effort have to be balanced. Thus application of Finite-Element analysis of individual clinical cases is limited. Voxel-based algorithms as opposed to contour-based algorithms allow an automated mesh-generation based on the image-data but their geometric precision is limited.

Methods

We developed an automated and universally applicable geometric mesh-generator that combines advantages of a voxel-based mesh generation with improved representation of the geometry by displacement of Finite-Element nodes on the object-surface. Models of an artificial 3D-pipe-section and a skullbase were generated with different mesh-densities using the newly developed geometric, unsmoothed and smoothed voxel generators. Finite Element analysis was performed and statistically compared for the individual mesh generators.

Results

The benefits of the developed mesh generator as compared to the existing algorithms were shown by statistical analysis. Comparison of the calculation results of the skullbase models showed mean element-energy errors of 2.24 Nmm, 2.14 Nmm, 1.22 Nmm for unsmoothed, smoothed and geometric voxel generators. The best representation of the anatomy as seen in the original data was achieved using the geometric voxel mesh-generator. Merely the oval foramen on the right was not rendered correctly, no pseudoforamina were generated. Furthermore the calculation of the analytic 3D-pipe-section model also resulted in the lowest element-energy error and volume error for the geometric generator as compared to smoothed and unsmoothed voxel generators.

Conclusions

The developed geometric mesh-generator is universally applicable. Furthermore it allows an automated and accurate modeling by combining advantages of the voxel-based mesh generation technique and improved geometric surface-modeling. It could therefore be beneficial for the application of Finite-Element analysis of individual clinical cases.