Melek Zeki, Mayerich David, Yuksel Cem, Keyser John
Computer Science, Texas A&M University, USA.
IEEE Trans Vis Comput Graph. 2006 Sep-Oct;12(5):1165-72. doi: 10.1109/TVCG.2006.197.
Thread-like structures are becoming more common in modern volumetric data sets as our ability to image vascular and neural tissue at higher resolutions improves. The thread-like structures of neurons and micro-vessels pose a unique problem in visualization since they tend to be densely packed in small volumes of tissue. This makes it difficult for an observer to interpret useful patterns from the data or trace individual fibers. In this paper we describe several methods for dealing with large amounts of thread-like data, such as data sets collected using Knife-Edge Scanning Microscopy (KESM) and Serial Block-Face Scanning Electron Microscopy (SBF-SEM). These methods allow us to collect volumetric data from embedded samples of whole-brain tissue. The neuronal and microvascular data that we acquire consists of thin, branching structures extending over very large regions. Traditional visualization schemes are not sufficient to make sense of the large, dense, complex structures encountered. In this paper, we address three methods to allow a user to explore a fiber network effectively. We describe interactive techniques for rendering large sets of neurons using self-orienting surfaces implemented on the GPU. We also present techniques for rendering fiber networks in a way that provides useful information about flow and orientation. Third, a global illumination framework is used to create high-quality visualizations that emphasize the underlying fiber structure. Implementation details, performance, and advantages and disadvantages of each approach are discussed.
随着我们在更高分辨率下对血管和神经组织进行成像的能力不断提高，线状结构在现代体数据集里变得越来越常见。神经元和微血管的线状结构在可视化方面带来了一个独特的问题，因为它们往往密集地分布在小体积的组织中。这使得观察者难以从数据中解读出有用的模式或追踪单个纤维。在本文中，我们描述了几种处理大量线状数据的方法，比如使用刀刃扫描显微镜（KESM）和连续块面扫描电子显微镜（SBF-SEM）收集的数据集。这些方法使我们能够从全脑组织的嵌入式样本中收集体数据。我们获取的神经元和微血管数据由延伸至非常大区域的细分支结构组成。传统的可视化方案不足以理解所遇到的大型、密集、复杂的结构。在本文中，我们介绍三种方法，以便用户有效地探索纤维网络。我们描述了使用在图形处理器（GPU）上实现的自定向表面来渲染大量神经元的交互技术。我们还展示了以提供有关流动和方向的有用信息的方式渲染纤维网络的技术。第三，使用全局光照框架来创建强调底层纤维结构的高质量可视化。文中讨论了每种方法的实现细节、性能以及优缺点。
