本文選題：多頻電阻抗成像 + 三維電阻抗成像　； 參考：《天津科技大學(xué)》2017年碩士論文

【摘要】：電阻抗斷層成像(Electrical Impedance Tomography,EIT)技術(shù)是近些年來發(fā)展出來的一種新的測量技術(shù),因?yàn)樗哂幸韵聝?yōu)點(diǎn):無輻射、非侵入性、響應(yīng)速度快,結(jié)構(gòu)簡單,成本低,并且在臨床監(jiān)測和工業(yè)測量等領(lǐng)域它具有廣闊的應(yīng)用前景。最近二十年來,雖然電阻抗斷層成像技術(shù)得到了長足的進(jìn)步,但是由于逆問題的嚴(yán)重病態(tài)性難以解決,導(dǎo)致重建圖像質(zhì)量不理想,圖像分辨率較差。同時,大部分研究人員將更多的精力投入到圖像重建算法研究上,系統(tǒng)硬件設(shè)計(jì)和軟件設(shè)計(jì)相對落后,成像軟件功能單一,顯示效果較差,成像速度較慢。本文采用神經(jīng)網(wǎng)絡(luò)技術(shù),建立神經(jīng)網(wǎng)絡(luò)模型,進(jìn)而優(yōu)化采集數(shù)據(jù),提高重建圖像質(zhì)量。其次研究設(shè)計(jì)了基于PXI總線的多頻電阻抗斷層成像平臺系統(tǒng),在物理實(shí)驗(yàn)水槽上完成了三維靜態(tài)和動態(tài)電阻抗成像試驗(yàn),最后對采集的多頻數(shù)據(jù)進(jìn)行了數(shù)據(jù)融合處理,進(jìn)一步提高了重建圖像的質(zhì)量。主要的研究工作如下:1.采用神經(jīng)網(wǎng)絡(luò)提高EIT成像質(zhì)量的方法。根據(jù)電阻抗成像原理,構(gòu)建了208-10-208的三層神經(jīng)網(wǎng)絡(luò)。將實(shí)驗(yàn)平臺上采集的實(shí)測數(shù)據(jù)分為訓(xùn)練數(shù)據(jù)和成像數(shù)據(jù),仿真數(shù)據(jù)作為期望值。首先將訓(xùn)練數(shù)據(jù)作為神經(jīng)網(wǎng)絡(luò)的輸入,對神經(jīng)網(wǎng)絡(luò)進(jìn)行訓(xùn)練,獲得神經(jīng)網(wǎng)絡(luò)參數(shù),建立神經(jīng)網(wǎng)絡(luò)模型,然后將成像數(shù)據(jù)作為訓(xùn)練好的神經(jīng)網(wǎng)絡(luò)的輸入,利用神經(jīng)網(wǎng)絡(luò)的輸出數(shù)據(jù)重建圖像,最后采用六種客觀指標(biāo)進(jìn)行評價。2.搭建了基于PXI總線的多頻電阻抗斷層成像平臺系統(tǒng)。該系統(tǒng)以NI公司的PXI設(shè)備為硬件基礎(chǔ),利用LabVIEW軟件編寫控制程序?qū)崿F(xiàn)數(shù)據(jù)的采集,最終將采集的數(shù)據(jù)上傳至PC機(jī)處理并調(diào)用Matlab軟件進(jìn)行圖像重建。該系統(tǒng)控制部分主要包括:激勵源板卡的頻率和幅值的設(shè)定,選定了四種不同的激勵頻率:100KHz、200 KHz、400 KHz、800KHz,幅值都是5mA;開關(guān)板卡的切換,采用二維模型相鄰激勵相鄰測量和三維模型非同層準(zhǔn)對角激勵同層相鄰測量的開關(guān)切換方式:數(shù)據(jù)采集卡采樣頻率的設(shè)置,選定5MHz, 10MHz,20MHz,40MHz四種采樣頻率,FPGA板卡采集程序的編程。3.使用LabVIEW軟件編寫了數(shù)字解調(diào)子VI程序,對采集到的電壓數(shù)據(jù)進(jìn)行數(shù)字解調(diào),得到其阻抗的實(shí)部和虛部信息(阻抗全信息),計(jì)算幅值,進(jìn)行EIT成像。4.制作了兩層共16電極(每層8電極)的試驗(yàn)水槽,利用COMSOL軟件建立了該試驗(yàn)水槽的仿真物理模型。對該物理模型進(jìn)行正問題求解,計(jì)算出其場域靈敏度矩陣,利用共軛度算法重建其三維仿真圖像。5.構(gòu)建EIT系統(tǒng),完成三維電阻抗成像試驗(yàn)。采用非同層準(zhǔn)對角激勵同層相鄰測量的工作模式,在試驗(yàn)水槽上進(jìn)行了三維數(shù)據(jù)采集及靜動態(tài)成像。成像結(jié)果能夠反映目標(biāo)物體在試驗(yàn)水槽中的形狀、位置、運(yùn)動狀態(tài)等信息。6.利用LabVIEW和MATLAB軟件對采集數(shù)據(jù)進(jìn)行離線處理,采用數(shù)據(jù)融合技術(shù)對采集到的多種頻率下的電壓數(shù)據(jù)進(jìn)行優(yōu)化處理,最大限度提高數(shù)據(jù)的準(zhǔn)確性和可靠性,改善整個系統(tǒng)的成像質(zhì)量。本論文的創(chuàng)新之處在于構(gòu)建了基于PXI總線技術(shù)的三維電阻抗成像多頻數(shù)據(jù)采集系統(tǒng),并利用神經(jīng)網(wǎng)絡(luò)和數(shù)據(jù)融合兩大方法對采集數(shù)據(jù)進(jìn)行了優(yōu)化處理,提高了圖像重建質(zhì)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.
【學(xué)位授予單位】：天津科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41;TP183

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