基于PXI总线的三维多频电阻抗成像系统研究

发布时间：2018-06-04 16:06

本文选题：多频电阻抗成像 + 三维电阻抗成像　；参考：《天津科技大学》2017年硕士论文

【摘要】：电阻抗断层成像(Electrical Impedance Tomography,EIT)技术是近些年来发展出来的一种新的测量技术,因为它具有以下优点:无辐射、非侵入性、响应速度快,结构简单,成本低,并且在临床监测和工业测量等领域它具有广阔的应用前景。最近二十年来,虽然电阻抗断层成像技术得到了长足的进步,但是由于逆问题的严重病态性难以解决,导致重建图像质量不理想,图像分辨率较差。同时,大部分研究人员将更多的精力投入到图像重建算法研究上,系统硬件设计和软件设计相对落后,成像软件功能单一,显示效果较差,成像速度较慢。本文采用神经网络技术,建立神经网络模型,进而优化采集数据,提高重建图像质量。其次研究设计了基于PXI总线的多频电阻抗断层成像平台系统,在物理实验水槽上完成了三维静态和动态电阻抗成像试验,最后对采集的多频数据进行了数据融合处理,进一步提高了重建图像的质量。主要的研究工作如下:1.采用神经网络提高EIT成像质量的方法。根据电阻抗成像原理,构建了208-10-208的三层神经网络。将实验平台上采集的实测数据分为训练数据和成像数据,仿真数据作为期望值。首先将训练数据作为神经网络的输入,对神经网络进行训练,获得神经网络参数,建立神经网络模型,然后将成像数据作为训练好的神经网络的输入,利用神经网络的输出数据重建图像,最后采用六种客观指标进行评价。2.搭建了基于PXI总线的多频电阻抗断层成像平台系统。该系统以NI公司的PXI设备为硬件基础,利用LabVIEW软件编写控制程序实现数据的采集,最终将采集的数据上传至PC机处理并调用Matlab软件进行图像重建。该系统控制部分主要包括:激励源板卡的频率和幅值的设定,选定了四种不同的激励频率:100KHz、200 KHz、400 KHz、800KHz,幅值都是5mA;开关板卡的切换,采用二维模型相邻激励相邻测量和三维模型非同层准对角激励同层相邻测量的开关切换方式:数据采集卡采样频率的设置,选定5MHz, 10MHz,20MHz,40MHz四种采样频率,FPGA板卡采集程序的编程。3.使用LabVIEW软件编写了数字解调子VI程序,对采集到的电压数据进行数字解调,得到其阻抗的实部和虚部信息(阻抗全信息),计算幅值,进行EIT成像。4.制作了两层共16电极(每层8电极)的试验水槽,利用COMSOL软件建立了该试验水槽的仿真物理模型。对该物理模型进行正问题求解,计算出其场域灵敏度矩阵,利用共轭度算法重建其三维仿真图像。5.构建EIT系统,完成三维电阻抗成像试验。采用非同层准对角激励同层相邻测量的工作模式,在试验水槽上进行了三维数据采集及静动态成像。成像结果能够反映目标物体在试验水槽中的形状、位置、运动状态等信息。6.利用LabVIEW和MATLAB软件对采集数据进行离线处理,采用数据融合技术对采集到的多种频率下的电压数据进行优化处理,最大限度提高数据的准确性和可靠性,改善整个系统的成像质量。本论文的创新之处在于构建了基于PXI总线技术的三维电阻抗成像多频数据采集系统,并利用神经网络和数据融合两大方法对采集数据进行了优化处理,提高了图像重建质量。
[Abstract]:Electrical Impedance Tomography (EIT) technology is a new measurement technology developed in recent years, because it has the following advantages: no radiation, noninvasive, fast response, simple structure, low cost, and it has a broad application prospect in the fields of clinical monitoring and industrial measurement. The latest twenty Although the technology of electrical impedance tomography has made great progress in the past year, the quality of the reconstructed image is not ideal and the resolution of the image is poor. At the same time, most researchers will devote more energy to the research of image reconstruction, and the hardware design and software design of the system are relatively falling. After that, the function of the imaging software is single, the display effect is poor and the speed of the imaging is slow. In this paper, neural network technology is used to establish the neural network model, and then to optimize the collection of data and improve the quality of the reconstructed image. Secondly, the multi frequency electrical impedance tomography flat system based on PXI bus is designed, and the three-dimensional static state is completed on the physical experiment tank. And dynamic electrical impedance imaging test, at last the data fusion processing of the collected multi frequency data is processed to further improve the quality of the reconstructed image. The main research work is as follows: 1. using the neural network to improve the quality of EIT imaging. According to the principle of electrical impedance imaging, the three layer neural network of the neural network is constructed. The experimental platform is adopted to pick up the experimental platform. The measured data of the set are divided into training data and imaging data, and the simulation data is expected. First, the training data is used as the input of the neural network, the neural network is trained, the neural network parameters are obtained, and the neural network model is established. Then the imaging data is used as the input of the trained neural network, and the output number of the neural network is used. According to the reconstructed image, the multi frequency electrical impedance tomography platform system based on PXI bus is built with six objective indexes. The system uses the PXI equipment of NI company as the hardware base, and uses the LabVIEW software to compile the control program to collect the data. Finally, the collected data is uploaded to the PC machine and the Matlab soft is called. The system controls the image reconstruction. The control part of the system mainly includes the setting of frequency and amplitude of the source plate card. Four different excitation frequencies are selected: 100KHz, 200 KHz, 400 KHz, and 800KHz, all 5mA; switching board cards, two dimensional model adjacent excitation adjacent measurement and three dimensional model non identical quasi diagonal excitation adjacent layer adjacent to the same layer Switch switching mode of measurement: setting of sampling frequency of data acquisition card, selecting four sampling frequencies of 5MHz, 10MHz, 20MHz, 40MHz, programming.3. of FPGA card acquisition program, using LabVIEW software to write digital demodulation VI program, digitally demodulate the collected voltage data and obtain the real and virtual information of impedance (full letter impedance). In the EIT imaging.4., the two layers of the total 16 electrodes (8 electrodes per layer) are made, and the simulation physical model of the test sink is established by using COMSOL software. The physical model is solved, the field domain sensitivity matrix is calculated, and the EIT system is constructed by the conjugate degree algorithm for the reconstruction of the 3D simulation image.5.. The three-dimensional electrical impedance imaging test is completed. The 3D data acquisition and static and dynamic imaging are carried out on the test sink by using the working mode of the non identical quasi diagonal excitation and the same layer adjacent measurement. The imaging results can reflect the shape, position and motion state of the target object in the test sink by using the LabVIEW and MATLAB software for the acquisition number. According to off-line processing, the data fusion technology is used to optimize the voltage data of the collected frequency, to maximize the accuracy and reliability of the data and improve the imaging quality of the whole system. The innovation of this paper is to build a multi frequency data acquisition system of 3D electrical impedance imaging based on PXI bus technology. In addition, two methods of neural network and data fusion are applied to optimize the collected data and improve the quality of image reconstruction.
【学位授予单位】：天津科技大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP391.41;TP183

【参考文献】