线性卡尔曼滤波算法在磁感应断层成像技术中的仿真研究  

Simulation study of linear Kalman filtering algorithm in magnetic induction tomography

作　　者：刘润生[1] 董秀珍[1] 刘锐岗[1] 

机构地区：[1]第四军医大学生物医学工程学院,西安710032

出　　处：《医疗卫生装备》2016年第1期1-4,共4页Chinese Medical Equipment Journal

基　　金：国家自然科学基金(61271101);全军医学科研"十二五"项目(BWS11Z011)

摘　　要：目的 :研究在磁感应断层成像(magnetic induction tomography,MIT)中使用多帧连续数据进行图像重建的线性卡尔曼滤波算法。方法:首先,对经典卡尔曼滤波算法进行改造,使其适用于MIT的需要;其次,为了评估线性卡尔曼滤波算法的性能,在仿真模型中对续变化的单个、2个和3个电导率扰动目标进行MIT仿真成像实验,并且绘制多个单元网格的电导率变化曲线。结果:获得了单个、2个和3个电导率扰动目标的重建图像和多个网格单元的电导率变化曲线。重建图像反映出了成像区域内电导率的分布,得到的电导率变化曲线和真实电导率变化曲线在变化趋势上基本一致,但扰动目标的位置在轴向上偏离中心。结论:在MIT中通过线性卡尔曼滤波算法可以有效地进行图像重建,并且有效地追踪电导率的变化,为进一步研究磁感应断层成像序列化图像重建算法打下基础。Objective To study linear Kalman filter algorithm （LKF） to reconstruct images using multiple and successive data frames in magnetic induction tomography （MIT）. Methods The classical Kalman filter algorithm was transformed and then applied it to MIT. The single, two and three perturbation targets were designed for MIT simulation experiments, and then the conductivity curve with multi unit grids was drawn. Results The reconstructed images of perturbations of different situations and the curve of the conductivity changes were obtained. The reconstructed images showed that the LKF was able to reflect the conductivity distribution and track the conductivity changes effectively. However, the positions of the perturbation target were offset.Conclosion LKF in MIT can be used to track the changes of conductivity distribution and reconstruct the images, and lay a foundation for further research on MIT serialized image reconstruction algorithm.

关 键 词：磁感应断层成像 线性卡尔曼滤波算法 重建图像 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]