摘要
电阻抗层析成像(EIT)的求解是一个非线性逆问题,其具有的不适定性是重建图像分辨率不高的原因之一。为改善不适定性,提高成像质量,引入先验信息和正则化项。利用器官组织电导率、胸腔器官分布结构等先验信息,构建正则化矩阵,并将正则化项引入扩展Kalman滤波(EKF)的状态空间表达式中,进行肺部癌变组织的图像重建。仿真结果表明,含有先验信息正则化项的扩展Kalman滤波算法可以改善癌变组织成像质量,降低图像相对误差。
Solution of electrical impedance tomography（ EIT） is a nonlinear inverse problem and its ill-posed problem is one of the reasons of low resolution of reconstructed images. In order to improve the ill-posedness and improve imaging quality,prior information and regularization term are introduced. The regularization matrix is reconstructed using the prior information,including the conductivity of organs and tissues and distribution structure of the human thorax. The regularization term is introduced into the state space expression of extended Kalman filtering（ EKF）,and the images of lung cancer issue are reconstructed. The simulation results indicate that the proposed method improves the imaging quality of the cancer tissue and reduce the relative error of the reconstructed images.
作者
李佳
岳士弘
王亚茹
LI Jia;YUE Shi-hong;WANG Ya-ru(Department of Automation,School of Electrical and Information Engineering,Tianjin University,Tiandin 300072,China)
出处
《传感器与微系统》
CSCD
2018年第10期22-24,共3页
Transducer and Microsystem Technologies
基金
国家自然科学基金资助项目(61573251)
关键词
电阻抗层析成像
扩展Kalman滤波
正则化矩阵
先验信息
electrical impedance tomography
extended Kalman filtering （EKF）
regularization matrix
priorinformation
