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质点弹簧模型是虚拟手术中常用模型之一,但由于其模型参数无明确物理意义,参数设定上存在诸多不便。由此提出基于遗传算法确定质点弹簧模型参数的方法。采用计算机辅助断层扫描数据(CAT data)确定质点质量值;弹簧弹性系数及阻尼系数通过遗传算法计算获得;并利用参考形变同模拟形变间差异大小作为适应度函数求得模型参数近似最优解。实验结果证明该方法能在较低计算成本的前提下获得弹簧参数的近似最优解,能够使虚拟模型较准确地再现实际模型形变效果。
The particle spring model is one of the commonly used models in virtual surgery. However, there are many inconveniences in parameter setting due to the lack of explicit physical meaning of its model parameters. Based on this, a method of determining the parameters of particle spring model based on genetic algorithm is proposed. The mass of mass was determined by CAT data. The spring elastic coefficient and damping coefficient were calculated by genetic algorithm. The approximate optimal solution of model parameters was obtained by using the difference between the reference deformation and the simulated deformation as the fitness function. The experimental results show that the proposed method can obtain the approximate optimal solution of the spring parameters under the condition of low computational cost and enable the virtual model to reproduce the actual model deformation effect more accurately.