东北大学学报（自然科学版）

东北大学学报:自然科学版 ›› 2015, Vol. 36 ›› Issue (9): 1246-1250.DOI: 10.3969/j.issn.1005-3026.2015.09.007

• 信息与控制 • 上一篇    下一篇

基于压力采集和FEM模型的软组织参数测量方法

廖祥云， 袁志勇， 陈二虎， 郑奇   

  1. (武汉大学 计算机学院， 湖北 武汉430072)
  • 收稿日期:2014-01-03 修回日期:2014-01-03 出版日期:2015-09-15 发布日期:2015-09-14
  • 通讯作者: 廖祥云
  • 作者简介:廖祥云(1989-)，男，湖南郴州人，武汉大学博士研究生； 袁志勇(1963-)，男，湖北武汉人，武汉大学教授，博士生导师.
  • 基金资助:
    国家自然科学基金资助项目(61372107)；国家重点基础研究发展计划项目(2011CB707904)； 北航虚拟现实技术与系统国家重点实验室开放课题基金资助项目(BUAA-VR-13KF-15).

Soft Tissue Parameter Measurement Based on Pressure Acquisition and FEM Model

LIAO Xiang-yun， YUAN Zhi-yong， CHEN Er-hu， ZHENG Qi   

  1. School of Computer， Wuhan University， Wuhan 430072， China.
  • Received:2014-01-03 Revised:2014-01-03 Online:2015-09-15 Published:2015-09-14
  • Contact: YUAN Zhi-yong
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摘要： 提出基于压力采集和FEM模型的软组织参数测量方法，搭建基于光学运动跟踪系统和压力采集模块的软组织参数测量平台，对软组织形变过程三维重建，通过带压力传感器的ARM采集板获取压力值，采用BP神经网络对传感器进行精度校正.基于软组织形变集合构建四面体有限元模型计算软组织的初始参数弹性模量与泊松比，并提出一种参数参照模型对初始参数进行修正，通过实验验证参数的准确性.实验结果表明，用所述方法求得的软组织参数计算的软组织形变与真实测量所得软组织形变的平均相对误差为1.03%~1.60%，符合实际工程应用对软组织形变的精度要求.

关键词: 软组织参数测量, 压力采集, 参数参照模型, 有限元, BP神经网络

Abstract: A method of soft tissue parameter measurement was proposed based on pressure acquisition and FEM model， which included a soft tissue parameter measurement platform based on optical motion tracking system and pressure acquisition module. The soft tissue deformation was reconstructed in three dimensions， and the pressures on the soft tissue were obtained by an ARM acquisition board with a pressure sensor whose precision was calibrated with the BP neural network. Then a tetrahedral finite element model was built to calculate the initial parameters (Young’s modulus and Poisson rates) of the soft tissue and a parameter reference model was proposed to obtain the modified parameters whose accuracy was verified experimentally. The experimental results indicated the average relative deviation between calculated deformation and measured deformation is 1.03%~1.60%， which satisfies the accuracy requirements of soft tissue deformation in practical engineering applications.

Key words: soft tissue parameter measurement, pressure acquisition, parameter reference model, finite element, BP neural network

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