Soza G, Grosso R, Nimsky C, Hastreiter P, Fahlbusch R, Greiner G
Computer Graphics Group, University of Erlangen-Nuremberg, Am Weichselgarten 9, Erlangen, Germany.
Int J Med Robot. 2005 Sep;1(3):87-95. doi: 10.1002/rcs.32.
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 nu 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.
在基于物理的模拟背景下,描述给定材料行为的可靠弹性参数是一个重要问题。在本文中,我们介绍了一种确定脑组织力学特性的方法。在将有限元模拟与图像配准相结合的迭代框架中估计弹性参数杨氏模量E和泊松比ν。在此框架内,模拟结果用两个弹性模量进行参数化,这两个弹性模量会自动变化,直到模拟数据与术中数据之间实现最佳图像对应。我们计算了6例脑组织的最佳力学特性。对所得值的统计分析表明结果具有良好的相关性,从而证明了该方法的价值。提出了一种结合模拟和配准来确定脑组织力学特性的方法。这有助于对软组织运动进行可靠的基于物理的模拟。