Determining Of The Elastic Modulus Of Biological Tissues Using The Smoothed Finite Element Methods And The Particle Swarm Algorithm

The pathological changes of biological tissue are closely related to the physical characteristics of biological tissue.In turn,studying changes in the physical properties of biological tissues can effectively reflect the lesions.This thesis aims to develop efficient numerical methods to identify the tumor in human tissue based on inverse model.The main objective of inverse model is to compute the Young's Modulus of tumor and normal tissue using smoothed finite element method(S-FEM).The mathematical model developed in this thesis provides a useful method to predict the status of human tissue.The introduction part introduces the existing tumor diagnostic methods.First,with the development of medicine,more and more computer aided techniques are used in quantitative analysis of physical parameters of biological tissues.Most of these techniques are based on the quantitative analysis of physical parameters of biological tissues.Secondly,in order to better use the finite element method to compute the modulus of elasticity,some domestic and foreign scholars consider the elastic modulus of biological tissue by combining the finite element method,the smooth finite element method and the optimization algorithm.In the second chapter,the basic ideas and solving process of the classical finite element method and the smooth finite element method are introduced.The theory of volume locking is discussed,and the method of eliminating the problem of volume locking is studied.In the third chapter,the problem of solving the elastic modulus is described as the inverse problem of the finite element problem.In combination with the optimization algorithm,a mathematical model for solving the inverse problem is established.Then,based on the displacement information of the biological tissue,the elastic modulus is inversed and the case is verified.The main contributions of this thesis include:(1)Develop a smoothed finite element model to overcome the volumetric locking in the human tissue model.The results have been validated by commercial software Abaqus successfully..(2)Combined with the smoothed finite element method,the inverse model to determine the Yound's modulus of human tissue is developed in this work.Based on this inverse model,we can identify the status of human tissue.3)In the inverse model,we have systematically studied the performance of Newton's method,particle swarm optimization and orthogonal particle swarm optimization in the prediction of Young's modulus of human tissue.The computational efficiency,convergence and stability of onverse model based on Newton's method,particle swarm optimization and orthogonal particle swarm optimization are studied in detail.