Determination of the elasticity parameters of brain tissue with combined simulation and registration

Authors

  • G Soza,

    Corresponding author
    1. Computer Graphics Group, University of Erlangen-Nuremberg, Am Weichselgarten 9, 91058 Erlangen, Germany
    2. Neurocenter, Department of Neurosurgery, University of Erlangen-Nuremberg, Schwabachanlage 6, 91054 Erlangen, Germany
    • Computer Graphics Group, University of Erlangen-Nuremberg, Am Weichselgarten 9, 91058 Erlangen, Germany and Neurocenter, Department of Neurosurgery, University of Erlangen-Nuremberg, Schwabachanlage 6, 91054 Erlangen, Germany
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  • R Grosso,

    1. Computer Graphics Group, University of Erlangen-Nuremberg, Am Weichselgarten 9, 91058 Erlangen, Germany
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  • C Nimsky,

    1. Neurocenter, Department of Neurosurgery, University of Erlangen-Nuremberg, Schwabachanlage 6, 91054 Erlangen, Germany
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  • P Hastreiter,

    1. Computer Graphics Group, University of Erlangen-Nuremberg, Am Weichselgarten 9, 91058 Erlangen, Germany
    2. Neurocenter, Department of Neurosurgery, University of Erlangen-Nuremberg, Schwabachanlage 6, 91054 Erlangen, Germany
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  • R Fahlbusch,

    1. Neurocenter, Department of Neurosurgery, University of Erlangen-Nuremberg, Schwabachanlage 6, 91054 Erlangen, Germany
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  • G Greiner

    1. Computer Graphics Group, University of Erlangen-Nuremberg, Am Weichselgarten 9, 91058 Erlangen, Germany
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Abstract

Reliable elasticity parameters describing the behavior of a given material are an important issue in the context of physically-based simulation. In this paper we introduce a method for the determination of the mechanical properties of brain tissue.

Elasticity parameters Young's modulus E and Poisson's ratio ν are estimated in an iterative framework coupling a finite element simulation with image registration. Within this framework, the outcome of the simulation is parameterized with both elasticity moduli that are automatically varied until optimal image correspondence between the simulated and the intraoperative data is achieved.

We calculated optimal mechanical properties of brain tissue in six cases. The statistical analysis of the obtained values showed a good correlation of the results, thus proving the value of the method. An approach combining simulation and registration for the determination of the mechanical brain tissue properties is presented. This contributes to performing reliable physically-based simulation of soft tissue movement. Copyright © 2005 John Wiley & Sons, Ltd.

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