基于非平稳度量的扩散磁共振成像心肌纤维重建算法研究

发布时间：2018-03-11 03:32

本文选题：扩散磁共振成像　切入点：心肌纤维　出处：《哈尔滨工业大学》2017年硕士论文　论文类型：学位论文

【摘要】：心脏疾病严重影响人们的生活和健康。相关医学研究表明,心脏生理特性和病理信息可由心肌纤维的结构反映。扩散磁共振成像是心脏纤维成像的重要手段,具有无创性和非侵入性特点。目前基于扩散磁共振的重建算法主要用于脑部研究,心肌纤维重建研究相对较少。本课题研究了向量数据的非平稳度量,提出了基于向量非平稳度量的快速行进纤维重建方法,并利用该方法对仿真和真实的心脏扩散磁共振数据进行纤维重建。首先,介绍了扩散磁共振张量成像原理,并依据该原理实现了多方向扩散加权图像合成扩散张量图像。选取张量模型来描述水分子的扩散运动,给出了张量的特征向量、各向异性系数及其它相关测度的计算方法和张量可视化方法,用于后续向量非平稳度量的研究和纤维重建。其次,研究了向量数据的非平稳度量。非平稳度量的概念经过拓展后能够应用于多种类型的数据中,本文给出向量数据非平稳度量的计算方法;将向量数据的非平稳度量与基于统计特征和最小向量分散度两种方法进行对比,实验表明非平稳度量在向量数据处理中,具有良好的抗噪性。最后,提出了基于非平稳度量的快速行进纤维重建方法。在构建时间场的过程中,对静态Hamilton-Jacobi方程的解法进行了改进,使得时间场的计算更加准确;利用向量非平稳度量构建速度函数的自适应系数,使改进的速度函数更符合心肌纤维的结构特性;对改进后的算法进行仿真数据实验和心脏数据实验验证,结果表明改进后的算法在重建心肌纤维方面能够很好地保持心肌纤维的局部一致性,在纤维曲率较大的区域能够很好地控制纤维前进的方向。
[Abstract]:Heart disease seriously affects people's life and health. Relevant medical studies have shown that cardiac physiological characteristics and pathological information can be reflected by the structure of myocardial fibers. Diffusion magnetic resonance imaging (DMR) is an important means of cardiac fiber imaging. It has the characteristics of non-invasive and non-invasive. At present, the reconstruction algorithm based on diffusive magnetic resonance is mainly used in brain research, but the research of myocardial fiber reconstruction is relatively few. The non-stationary measurement of vector data is studied in this paper. In this paper, a fast fiber reconstruction method based on vector nonstationary measurement is proposed, and the simulation and real diffusion magnetic resonance data are reconstructed using this method. Firstly, the principle of diffusive magnetic resonance Zhang Liang imaging is introduced. According to this principle, the diffusion Zhang Liang image is synthesized by using multi-directional diffusion weighted image, and then the Zhang Liang model is selected to describe the diffusion motion of water molecules, and the eigenvector of Zhang Liang is given. The calculation method of anisotropic coefficient and other related measures and Zhang Liang visualization method are used in the research of nonstationary measure of follow-up vector and fiber reconstruction. Secondly, This paper studies the nonstationary metric of vector data. The concept of nonstationary metric can be applied to many kinds of data after being extended. In this paper, the calculation method of nonstationary metric of vector data is given. The non-stationary metric of vector data is compared with the two methods based on statistical feature and minimum vector dispersion. The experimental results show that the non-stationary metric has good noise resistance in vector data processing. A fast moving fiber reconstruction method based on nonstationary metric is proposed. In the process of constructing time field, the solution of static Hamilton-Jacobi equation is improved to make the calculation of time field more accurate. The adaptive coefficient of the velocity function is constructed by vector nonstationary measurement to make the improved velocity function more accord with the structure characteristics of myocardial fiber, and the improved algorithm is verified by simulation data experiment and heart data experiment. The results show that the improved algorithm can keep the local consistency of myocardial fiber in the reconstruction of myocardial fiber and control the direction of fiber forward in the region with larger fiber curvature.
【学位授予单位】：哈尔滨工业大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：R445.2;TP391.41

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