64028 Computational Time Reduction for Neurosurgical Training System Based on Finite Element Method(Biomechanics)

Abstract

Authors are working on the development of the neurosurgical training system based on force feedback device. In the training system, it is necessary to generate not only visual view of the surgical scene similar to the surgical field but also tactile sensation due to intraoperative interaction between the brain tissue and the surgical instruments (brain spatula, suction, forceps, scissors, etc.). The development of the neurosurgical training system will greatly contribute to neurosurgery, since it enables neurosurgeons to improve surgical technique safely at any time. Furthermore, the surgeon can repeat the practice of the operation to a few cases by using the training system. In order to predict intraoperative brain tissue deformation due to retraction of cerebellum with spatula, our research group developed three-dimensional finite element brain model. However, long computation time was required. Computation time reduction is essential for the real-time simulation based on finite element analysis. The goal of this study is developing the novel finite element model which can achieve drastic computation time reduction of brain shift simulation for surgical training system by using static condensation. Static condensation is one of the methods for reducing the degree of freedom of the finite element model. Our research group demonstrated the usefulness of linear elastic model in gravity induced brain tissue deformation simulation for the reduction of computation time in the previous work. Then, the finite element analysis in the linear elastic medium is introduced in this study. Tetrahedral mesh is generated and the simulation results obtained by the proposed finite element model is compared with that obtained by the previous developed model. Illustrative brain tissue deformation simulation results will show the availability of the proposed model.