基于混合模型电容层析成像系统敏感矩阵建立

电容层析成像系统的敏感场是一个非线性场,敏感场的分布数据作为图像重建所需的先验数据必须通过理论计算得到,对于同一套电容测量值,不同的归一化模型对重建图像的质量影响有明显的差异,为了提高重构图像质量,对归一化模型进行研究是十分有必要的.采取有限元法建立了传感器敏感场的数学模型,给出了8电极敏感场的分布,提出了一种新的基于正灵敏度归一化混合模型来提高图像的质量,并将此模型与传统的并联模型和串联模型进行了归一化电容值的比较,采用了Landweber迭代算法进行图像重建,给出了基于三种模型评价图像质量的迭代比较.仿真结果表明:验证了建立的有限元模型的正确性,采用基于正灵敏度归一化混合模型,可以获得比较均匀的灵敏场分布且符合实际,重建图像具有较高的精确度.

Sensitivity Matrix Construction in Electrical Capacitance Tomography System Based on Mixed Models

Sensitivity field in electrical capacitance tomography system is a nonlinear field.The data of sensitivity distribution which is got by theoretical calculation can be used as prior experimental knowledge for image reconstruction.For capacitance measurements of the same set,different normalization model for the quality of reconstructing image effects are clearly different.To improve the quality of image reconstruction,it is necessary to analyze the normalization model of sensitivity.Finite element method is adopted to establish the mathematical model and the sensitivity distribution of an 8-electrode sensor,and a novel mixed normalization model with positive sensitivity areas is put forward to improve the quality of images,which is compared to the normalized capacitance of the traditional parallel model and serial model.Using Landweber iteration algorithms,an iteration comparison is given based on three normalization.Simulation results show the finite element model is correct,and uniform sensitivity distribution has been obtained by the mixed normalization modes.At the same time,the result of image reconstruction has high precision.