本文選題：腦磁感應(yīng)斷層成像　切入點(diǎn)：顱腦模型　出處：《沈陽工業(yè)大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：腦磁感應(yīng)斷層成像(BMIT)是一種以顱內(nèi)組織電導(dǎo)率為成像目標(biāo)的新興醫(yī)學(xué)成像技術(shù),由于它的非接觸、無創(chuàng)性使其在當(dāng)今醫(yī)學(xué)成像領(lǐng)域具有潛在的優(yōu)勢。為滿足實(shí)時(shí)監(jiān)測顱內(nèi)病灶的臨床要求,本文在分析現(xiàn)有的BMIT圖像重建算法的基礎(chǔ)上,設(shè)計(jì)了一套濾波反投影迭代重建算法,并基于BMIT的檢測數(shù)據(jù),提取出對于顱內(nèi)電導(dǎo)率變化敏感性高的一維量化監(jiān)測指標(biāo),標(biāo)定顱內(nèi)出血狀態(tài)。本文首先根據(jù)顱腦解剖結(jié)構(gòu)仿真出三維三層簡化顱腦模型,基于該顱腦模型建立了BMIT仿真系統(tǒng),通過正問題的計(jì)算得到檢測線圈處的相位數(shù)據(jù),將測量數(shù)據(jù)用于BMIT成像和量化指標(biāo)的分析中,并研究了顱腦外層結(jié)構(gòu)對BMIT信號檢測的影響。其次,依據(jù)反投影算法和迭代算法相結(jié)合的設(shè)計(jì)思想,提出一種基于濾波反投影的腦磁感應(yīng)迭代重建算法,將濾波反投影算法重建的電導(dǎo)率分布經(jīng)過校正系數(shù)處理后作為一步牛頓迭代算法的初值,并采用特征門限修正法改善靈敏度矩陣的病態(tài)程度,該算法提高了重建圖像的分辨率,加快了成像速度,是一種快速有效的BMIT重建算法。通過建立量化仿真平臺對測量的相位數(shù)據(jù)進(jìn)行分析處理,得到總相位值、總相對變化量、總正對點(diǎn)相位值和回歸相位平方和四種量化監(jiān)測指標(biāo),實(shí)現(xiàn)BMIT對病程變化量的估算。最后,基于實(shí)際測量系統(tǒng)進(jìn)行圖像和量化指標(biāo)的聯(lián)合分析,實(shí)驗(yàn)結(jié)果表明本文提出的一維量化監(jiān)測指標(biāo)和BMIT重建圖像結(jié)合使用,互為補(bǔ)充,不僅能夠?qū)崟r(shí)提供顱內(nèi)整體電導(dǎo)率的變化趨勢和大小,同時(shí)也提供了電導(dǎo)率局部變化的位置信息,為顱腦磁感應(yīng)斷層成像技術(shù)應(yīng)用于臨床監(jiān)護(hù)奠定了基礎(chǔ)。
[Abstract]:Brain Magnetic Induction Tomography (BMIT) is a new medical imaging technology which aims at the electrical conductivity of intracranial tissue. It has potential advantages in the field of medical imaging. In order to meet the clinical requirements of real-time monitoring of intracranial lesions, a set of filtered backprojection iterative reconstruction algorithm is designed based on the analysis of existing BMIT image reconstruction algorithms. Based on the detection data of BMIT, one dimensional quantitative monitoring index with high sensitivity to the change of intracranial conductivity was extracted, and the state of intracranial hemorrhage was calibrated. In this paper, a three-dimensional three-layer simplified craniocerebral model was first simulated according to the anatomical structure of the brain. Based on the brain model, the BMIT simulation system is established. The phase data of the detection coil are obtained by the calculation of the positive problem, and the measurement data are used in the analysis of the BMIT imaging and quantification index. The influence of brain outer layer structure on BMIT signal detection is studied. Secondly, according to the design idea of combining backprojection algorithm and iterative algorithm, a brain magnetic induction iterative reconstruction algorithm based on filtering backprojection is proposed. The conductivity distribution reconstructed by the filter back-projection algorithm is treated as the initial value of the one-step Newton iterative algorithm after the correction coefficient, and the pathological degree of the sensitivity matrix is improved by using the characteristic threshold correction method, which improves the resolution of the reconstructed image. It is a fast and effective BMIT reconstruction algorithm. By establishing a quantitative simulation platform to analyze and process the measured phase data, the total phase value and the total relative variation can be obtained. Total positive point phase value and regression phase square sum of four quantitative monitoring indicators to achieve the BMIT to estimate the course of disease change. Finally, based on the actual measurement system for image and quantitative indicators of joint analysis, The experimental results show that the one-dimensional quantitative monitoring index proposed in this paper can be used in combination with BMIT reconstruction images, which can not only provide the change trend and magnitude of the whole intracranial conductivity in real time, but also complement each other. At the same time, the location information of local change of electrical conductivity is also provided, which lays a foundation for the application of brain magnetic induction tomography in clinical monitoring.
【學(xué)位授予單位】：沈陽工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41;R743.34

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