基于HARDI数据的脑纤维可视化系统设计

发布时间：2018-06-20 19:35

本文选题：HARDI + 密集纤维绘制　；参考：《浙江工业大学》2017年硕士论文

【摘要】：医学数据可视化在医疗辅助诊断领域有着非常重要的应用。伴随着扩散加权磁共振成像技术(DW-MRI)的发展,研究人员通过测量水分子在纤维当中扩散运动的各向异性,利用扩散张量成像(DTI)模型重构出脑白质中微米级的大脑纤维。这项技术可以用于帕金森症、抑郁症、老年痴呆症等疾病的辅助诊断中。由于DTI模型本身存在的限制,脑纤维图像不能够反应出纤维交叉的情况。为了向医务人员提供更加精确的辅助图像,高角分辨率扩散张量成像技术(HARDI)得以应用。该项技术在DW-MRI的基础上通过高阶张量等模型的应用,能够精确的反应出在各项异性值较小的区域内纤维的交叉走向。临床上的原始数据通过建模方法获得的数据也被称为HARDI数据。为了能够更好的对HARDI数据进行可视化展示,交互以及分析,本文设计并实现基于HARDI数据的大脑纤维可视化系统。在该系统中通过内部的可视化技术对绘制效果进行增强,通过散布矩阵和聚类等数学方法对可视结果进行分析,并且加入了外部体感交互设备进行交互增强等。本文的主要工作和成果如下:(1)设计出了一种针对于脑纤维数据可视化软件的系统框架。该框架包含硬件基础层,存放数据的数据存储层,对数据进行预处理操作和绘制操作的数据处理层以及可视化显示、分析和交互的展示层。(2)对纤维绘制结果添加了绘制增强和可视分析等方法。大脑纤维数据量大,密度高导致用户对于纤维的感知能力下降,本系统通过添加光照、抗锯齿等方法增强纤维绘制的效果并且应用局部分析和散布矩阵等方法对纤维进行筛选从而减少遮挡。此外本系统应用快速峰值密度搜索的聚类方法对绘制结果进行聚类分析。(3)为系统添加体感设备的交互。传统交互都是基于鼠标键盘等,最近体感技术的兴起标志着更加符合人体工程学的交互手段会出现。本系统增加了通过Leap Motion的空间手势交互,并在此基础上提出了针对球体数据的交互手势。最后,我们实现了脑纤维可视化的原型系统,论证了我们设计方法的合理性,而且系统拥有良好的拓展性,便于后续的开发和升级。
[Abstract]:The visualization of medical data has a very important application in the field of medical auxiliary diagnosis. With the development of diffusion-weighted magnetic resonance imaging (DW-MRI), the researchers reconstruct micron brain fibers in white matter by measuring the anisotropy of diffusion motion of water molecules in fibers and using diffusion Zhang Liang imaging (DTI) model. The technique can be used to assist the diagnosis of Parkinson's disease, depression, and Alzheimer's disease. Because of the limitation of DTI model, the image of brain fiber can not reflect the situation of fiber crossing. In order to provide medical staff with more accurate auxiliary images, high resolution diffusion Zhang Liang imaging technology has been applied. On the basis of DW-MRI, this technique can accurately reflect the cross direction of fibers in different regions with low heterogeneity by the application of high-order Zhang Liang and other models. The original clinical data obtained by modeling method is also known as HARDI data. In order to visualize, interact and analyze HARDI data, this paper designs and implements a visualization system of brain fiber based on HARDI data. In this system, the rendering effect is enhanced by the internal visualization technology, the visual results are analyzed by the mathematical methods such as scatter matrix and clustering, and the external somatosensory interactive equipment is added to enhance the interaction. The main work and results of this paper are as follows: 1) A software framework for visualization of brain fiber data is designed. The framework includes hardware base layer, data storage layer for storing data, data processing layer for data preprocessing and drawing operation, and visual display. Analysis and interactive presentation layer. 2) adding rendering enhancement and visual analysis to fiber rendering results. The large amount of data and the high density of the fibers in the brain lead to a decrease in the ability of users to perceive the fibers. Antisawtooth and other methods are used to enhance the effect of fiber rendering and local analysis and dispersion matrix are used to screen the fibers to reduce the occlusion. In addition, the system uses the fast peak density search clustering method to analyze the rendering results, and adds the interaction of the somatosensory equipment to the system. Traditional interaction is based on mouse and keyboard. The recent rise of somatosensory technology marks the emergence of more ergonomic interaction. In this system, the spatial gesture interaction through Leap Motion is added, and an interactive gesture for sphere data is proposed. Finally, we realized the prototype system of brain fiber visualization, demonstrated the rationality of our design method, and the system has good expansibility, which is convenient for further development and upgrade.
【学位授予单位】：浙江工业大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：R445.2;TP391.41

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