Using region-of-interest based finite element modelling for brain-surgery simulation

  • Kim Vang Hansen
  • Ole Vilhelm Larsen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1496)


Brain surgery simulation requires a mathematical model of the geometric and elastic properties of the entire brain. To allow for realtime manipulation of the model it is necessary to differentiate the level of accuracy between different subparts of the brain model. A Finite Element Model (FEM) of the brain is presented capable of differentiating the spatial and temporal accuracy in different parts of the model. In a user defined region-of-interest around the surgical target point a dynamic FEM model is used to give high accuracy. The remaining parts of the brain is modelled by a static FEM model having less accuracy. The two models are integrated into one model for the entire brain using Condensation. In the context of our early version of a brain surgery simulator we have tested the condensed model versus a full dynamic model of the brain. Promising results concerning spatial error and execution time are shown.


Spatial Error Steady State Error Entire Brain Brain Surgery Surgery Simulation 
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  1. 1.
    Morten Bro-Nielsen and Stephane Cotin. Real-time volumetric deformable models for surgery simulation using finite elements and condensation. Computer Graphics Forum, 15(3):57–66, 1996.CrossRefGoogle Scholar
  2. 2.
    Philippe G. Ciarlet. Mathematical Elasticity, Volume I: Three-Dimensional Elasticity. Elsevier Science Publisher B.V., 1988.Google Scholar
  3. 3.
    Robert D. Cook, David S. Malkus, and Michael E. Plesha. Concepts and Applications of Finite Element Analysis. John Wiley & Sons, 1989.Google Scholar
  4. 4.
    S. Cotin, H. Delingette, and N. Ayache. Volumetric deformable models for simulation of laparoscopic surgery. In Computer Assisted Radiology (CAR’ 96), 1996.Google Scholar
  5. 5.
    S. A. Cover, N. F. Ezquerra, J. F. O’Brien, R. Rowe, T. Gadacz, and E. Palm. Interactively deformable models for surgery simulation. IEEE Computer Graphics and Applications, 13:68–75, 1993.CrossRefGoogle Scholar
  6. 6.
    Sarah F. F. Gibson. 3D ChainMail: a Fast Algorithm for Deforming Volumetric Objects., 1996.Google Scholar
  7. 7.
    Sarah Gibson et. al. Volumetric object modeling for surgical simulation. Medical Image Analysis, 2:121–132, june 1998.CrossRefPubMedGoogle Scholar
  8. 8.
    Kim Vang Hansen, Martin Stumpf Eskildsen, and Ole Vilhelm Larsen. Region-of-interest based finite element modelling of the human brain — an approach to brain-surgery simulation. In Proceedings of the 14th International Conference on Pattern Recognition, (ICPR’98), August 1998.Google Scholar
  9. 9.
    Kenneth H. Huebner. The Finite Element Method for Engineers. John Wiley & Sons, 1975.Google Scholar
  10. 10.
    Erwin Keeve, Sabine Girod, and Bernd Girod. Craniofacial surgery simulation. Telecommunications Institute, University of Erlangen-Nuremberg, German, 1996.CrossRefGoogle Scholar
  11. 11.
    U. Kuhn, Kühnapfel, H.-G. Krumm, and B. Neisius. The Karlsruhe Endoscopic Surgery Trainer — A “Virtual Reality” based Training System for Minimal Invasive Surgery. CAR, 1996.Google Scholar
  12. 12.
    Richard M. Satava. Advanced simulation technologies for surgical education. Bulletin of the American College of Surgeons, 81, 1996.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Kim Vang Hansen
    • 1
  • Ole Vilhelm Larsen
    • 1
  1. 1.Virtual Centre for Health Informatics Department of Medical Informatics and Image AnalysisAalborg UniversityDenmark

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