基于磁感应的颅内出血图像表示方法研究

发布时间：2018-03-11 11:14

本文选题：脑磁感应断层成像　切入点：颅脑模型　出处：《沈阳工业大学》2017年硕士论文　论文类型：学位论文

【摘要】：脑磁感应断层成像(BMIT)是一种以颅内组织电导率为成像目标的新兴医学成像技术,由于它的非接触、无创性使其在当今医学成像领域具有潜在的优势。为满足实时监测颅内病灶的临床要求,本文在分析现有的BMIT图像重建算法的基础上,设计了一套滤波反投影迭代重建算法,并基于BMIT的检测数据,提取出对于颅内电导率变化敏感性高的一维量化监测指标,标定颅内出血状态。本文首先根据颅脑解剖结构仿真出三维三层简化颅脑模型,基于该颅脑模型建立了BMIT仿真系统,通过正问题的计算得到检测线圈处的相位数据,将测量数据用于BMIT成像和量化指标的分析中,并研究了颅脑外层结构对BMIT信号检测的影响。其次,依据反投影算法和迭代算法相结合的设计思想,提出一种基于滤波反投影的脑磁感应迭代重建算法,将滤波反投影算法重建的电导率分布经过校正系数处理后作为一步牛顿迭代算法的初值,并采用特征门限修正法改善灵敏度矩阵的病态程度,该算法提高了重建图像的分辨率,加快了成像速度,是一种快速有效的BMIT重建算法。通过建立量化仿真平台对测量的相位数据进行分析处理,得到总相位值、总相对变化量、总正对点相位值和回归相位平方和四种量化监测指标,实现BMIT对病程变化量的估算。最后,基于实际测量系统进行图像和量化指标的联合分析,实验结果表明本文提出的一维量化监测指标和BMIT重建图像结合使用,互为补充,不仅能够实时提供颅内整体电导率的变化趋势和大小,同时也提供了电导率局部变化的位置信息,为颅脑磁感应断层成像技术应用于临床监护奠定了基础。
[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.
【学位授予单位】：沈阳工业大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP391.41;R743.34

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