Advertisement

Estimating Mechanical Brain Tissue Properties with Simulation and Registration

  • Grzegorz Soza
  • Roberto Grosso
  • Christopher Nimsky
  • Guenther Greiner
  • Peter Hastreiter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3217)

Abstract

In this work a new method for the determination of the mechanical properties of brain tissue is introduced. Young’s modulus E and Poisson’s ratio ν are iteratively estimated based on a finite element model for brain shift and on the information contained in pre- and intraoperative MR data after registration. In each iteration, a 3D dataset is generated according to the displacement vector field resulting from a numerical simulation of the intraoperative brain deformation. This reconstruction is parametrized by elastic moduli of tissue. They are automatically varied in order to achieve the best correspondence between the grey value distribution in the reconstructed image and the intensity entropy in the MR image of the brain undergoing deformation. This work contributes to the difficult problem of defining correct mechanical parameters to perform reliable model calculations of brain deformation. Proper boundary conditions that are crucial in this context are also addressed.

Keywords

Coarse Grid Normalize Mutual Information Brain Shift Proper Boundary Condition Increase Pore Pressure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Hagemann, A., Rohr, K., Stiehl, H.S., Spetzger, U., Gilsbach, J.M.: Biomechanical Modeling of the Human Head for Physically Based, Non-Rigid Image Registration. IEEE Trans. Med. Imaging 18(10), 875–884 (1999)CrossRefGoogle Scholar
  2. 2.
    Hastreiter, P., Ertl, T.: Integrated Registration and Vis ualization of MedicalImage Data. In: Proc. CGI,, pp. 78–85 Hannover, Germany (1998)Google Scholar
  3. 3.
    Kaczmarek, M., Subramanian, R.P., Neff, S.R.: The Hydromechanics of Hydrocephalus: Steady-State Solutions for Cylindrical Geometry. Bull. Math. Biol. 59, 295–323 (1997)zbMATHCrossRefGoogle Scholar
  4. 4.
    Krouskop, T.A., Wheeler, T.M., Kallel, F., Garra, B., Hall, T.: The Elastic Moduli of Breast and Prostate Tissues Under Compression. Ultrason Imaging 20, 151–159 (1998)Google Scholar
  5. 5.
    Manduca, A., Lake, D.S., Kruse, S.A., Ehman, R.L.: Spatio-temporal Directional Filtering for Improved Inversion of MR Elastography Images. Med. Image Anal. 7(4), 465–473 (2003)CrossRefGoogle Scholar
  6. 6.
    Miga, M.: A New Approach to Elastography Using Mutual Information and Finite Elements. Phys. Med. Biol. 48, 467–480 (2003)CrossRefGoogle Scholar
  7. 7.
    Miga, M., Paulsen, K., Hoopes, P., Kennedy, F., Hartov, A.: In Vivo Modeling of Intersitial Pressure in the Brain under Surgical Load Using Finite Elements. J. Biomech. Eng. 122, 354–363 (2000)CrossRefGoogle Scholar
  8. 8.
    Miller, K., Chinzei, K.: Simple Validation of Biomechanical Models of Brain Tissue. In: Proc. European Society of Biomechanics Conference, vol. 31, p. 104. Elsevier Science, Amsterdam (1998)Google Scholar
  9. 9.
    Miller, K., Chinzei, K., Orssengo, G., Bednarz, P.: Mechanical Properties of Brain Tissue In-vivo: Experiment and Computer Simulation. J. Biomechanics 33(11), 1369–1376 (2000)CrossRefGoogle Scholar
  10. 10.
    Ophir, J., Kallel, F., Varghese, T., Konofagou, E., Alam, S.K., Krouskop, T., Garra, B., Righetti, R.: Elastography. Comptes Rendus de l’Acadmie des Sciences - Series IV - Physics 2(8), 1193–1212 (2002)CrossRefGoogle Scholar
  11. 11.
    Press, W.H., Teukolsky, S.A., Vetterling, W.T., Flannery, B.P.: Numerical Recipes in C++. Cambridge University, New York (2002)Google Scholar
  12. 12.
    Sandrin, L., Tanter, M., Catheline, S., Fink, M.: Shear Modulus Imaging with 2-DTransient Elastography. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 49(4), 426–435 (2002)CrossRefGoogle Scholar
  13. 13.
    Showalter, R.E.: Diffusion in Poro-Elastic Media. J. Math. Anal. Appl. 251, 310–340 (2000)zbMATHCrossRefMathSciNetGoogle Scholar
  14. 14.
    Soza, G., Grosso, R., Hastreiter, P., Labsik, U., Nimsky, R. Fahlbusch, Greiner, G.: Fast and Adaptive Finite Element Approach for Modeling Brain Shift. In: Proc CURAC (Dt. Gesell. Computer and Robotorassistierte Chirurgie) (2003)Google Scholar
  15. 15.
    Studholme, C., Hill, D.L.G., Hawkes, D.J.: An Overlap Invariant Entropy Measure of 3D Medical Image Alignment. Pattern Recogn. 32(1), 71–86 (1999)CrossRefGoogle Scholar
  16. 16.
    Tanner, C., Degenhard, A., Schnabel, J.A., Hayes, C., Sonoda, L.I., Leach, M.O., Hose, D.R., Hill, D.L.G., Hawkes, D.J.: A Comparison of Biomechanical Breast Models: a Case Study. In: Proc. SPIE Medical Imaging 2002, vol. 4683, pp. 1807–1818 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Grzegorz Soza
    • 1
  • Roberto Grosso
    • 1
  • Christopher Nimsky
    • 2
  • Guenther Greiner
    • 1
  • Peter Hastreiter
    • 1
    • 2
  1. 1.Computer Graphics GroupUniversity of Erlangen-NurembergErlangenGermany
  2. 2.Neurocenter, Department of NeurosurgeryUniversity of Erlangen-Nuremberg 

Personalised recommendations