基于DT-MRI数据的心肌纤维可视化及分析系统研究与实现

核磁共振弥散张量成像（Diffusion Tensor Magnetic Resonance Imaging,DT-MRI）是近年来提出的一项新的医学影像技术。通过利用核磁共振技术测量细胞内多个方向上水分子弥散运动的特征,可以全面充分研究活体组织微细结构。基于犬心室的DT-MRI数据集开展了三维心肌纤维结构的可视化方法及其可视化系统设计与实现的研究。首先利用阈值滤波、中值滤波和邻域平均法对原始DT-MRI数据进行去噪预处理,然后利用体绘制技术、图元显示法和纤维跟踪法对犬心室的DT-MRI数据进行可视化。最后对基于DT-MRI数据的心肌纤维可视化应用进行了详细的需求分析和系统设计,基于VTK（Visualization Toolkit）实现一个DT-MRI数据的心肌纤维可视化和分析系统。实验结果表明该系统具有良好的交互性,为研究心脏结构提供了一个有力可视化工具。     

基于DT-MRI数据的心肌纤维可视化及分析系统研究与实现

核磁共振弥散张量成像(Diffusion Tensor Magnetic Resonance Imaging,DT-MRI)是近年来提出的一项新的医学影像技术。通过利用核磁共振技术测量细胞内多个方向上水分子弥散运动的特征,可以全面充分研究活体组织微细结构。基于犬心室的DT-MRI数据集开展了三维心肌纤维结构的可视化方法及其可视化系统设计与实现的研究。首先利用阈值滤波、中值滤波和邻域平均法对原始DT-MRI数据进行去噪预处理,然后利用体绘制技术、图元显示法和纤维跟踪法对犬心室的DT-MRI数据进行可视化。最后对基于DT-MRI数据的心肌纤维可视化应用进行了详细的需求分析和系统设计,基于VTK(Visualization Toolkit)实现一个DT-MRI数据的心肌纤维可视化和分析系统。实验结果表明该系统具有良好的交互性,为研究心脏结构提供了一个有力可视化工具。     

基于多通道小波的DTI图像恢复

扩散张量图像中存在的赖斯噪声给张量计算和脑白质追踪等带来严重影响。为了减少噪声影响,该文采用多通道小波对扩散加权图像进行恢复,采用峰值信噪比来定量地评估本滤波器消除赖斯噪声的性能。基于模拟和真实数据对张量场的表面扩张系数等进行了计算并进行人脑白质纤维追踪。把该去噪方法和单通道小波方法进行比较,实验结果表明,提出的滤波器具有更好的噪声性能。     

基于复扩散过程的DTI图像恢复和纤维追踪

为了消除扩散加权图像中广泛存在的赖斯噪声,采用了复扩散滤波器。基于模拟数据的实验结果表明,在信噪比低的情况下复扩散滤波器具有更好的消除赖斯噪声的效果。运用本滤波器对脑部DTI图像进行去噪处理并对去噪后的图像进行纤维追踪,结果显示复扩散滤波器能够有效消除噪声影响从而使得追踪到的脑白质纤维数量增多,长度更长。     

采用贝叶斯决策概率型DT-MRI的脑白质纤维追踪成像

弥散张量磁共振成像(DT-MRI)的脑白质纤维追踪成像可无创重建脑白质纤维的三维结构,而现有追踪成像方法一般仅考虑局部纤维的弥散倾向,对纤维束几何结构的综合考虑不足,为此提出一种贝叶斯决策概率型的纤维追踪成像算法.该算法通过纤维束当前体素的弥散张量方向和纤维束几何结构信息,利用贝叶斯决策理论估算追踪下一体素的方向概率分布;按照概率分布对纤维束进行加权采样,重建纤维束的三维结构图像.最后利用文中算法在合成弥散张量数据上进行了成像仿真,在真实脑部DT-MRI数据上进行了成像实验.仿真和实验结果表明,该算法能实现预期的脑白质纤维追踪成像,比现有追踪成像方法结果更可靠,可重复性更强.     

基于稀疏正则化的高维数据可视化分析技术

高维数据可视化分析是数据分析与可视化领域的研究热点,传统的降维方法得到的低维空间往往难以解释,不利于人们对高维数据的可视化分析与探索。提出一种新的可视化解释器(Explainer)方法,将L1稀疏正则化特征选取引入到高维数据的可视化处理过程中,建立起高层语义标签与少量的关键特征之间的联系。通过可视化设计与实验验证了该方法可以有效改善高维数据的可视化分析性能。     

基于3D Slicer的弥散张量成像方法研究与应用

弥散张量成像技术已经应用于临床,而利用弥散张量数据进行纤维束追踪是近年来研究的热点问题,本文基于3D Slicer平台对弥散张量成像方法进行了研究,详细介绍了MR图像弥散张量成像的分析处理流程,对医学诊断有很重要的参考价值。     

参数化标准件的可视化生成

本文介绍了在特征可视化生成系统ＶＰＳＦ支持下，通过约束方法与构成标准件的实体关联，不需编程就可生成任一复杂的参数化标准件，该可视生成方法具有方便、直观及面向用户等特点。     

基于Web的大数据可视化系统

随着互联网技术的不断发展,各行业数据呈爆发式增长。为了使企业更加方便有效利用这些数据,数据可视化系统应运而生。但是,现有的数据可视化系统大部分针对特定场景开发,易用性差、学习曲线长、维护成本较高。笔者提出的基于Web的大数据可视化系统,具有灵活的交互方式,使用户在丰富图表类型的基础上,快速实现数据可视化,并支持本地CSV上传和JDBC数据源连接。该系统以组件化的方式进行开发,提高了开发效率和系统可维护性。     

基于OpenGL的球体生成算法可视化仿真研究

研究计算机图像的三维可视化优化问题,针对图像生成慢的缺点,为了仿真计算机图形学中三维球体的生成过程,提高图像生成速度,提出了一种三角形分割的算法,实现了球体生成算法可视化的仿真系统CM.系统的实现,摒弃了原来可视化系统中依靠计算最佳逼近"像素点"的位置来显示图形的方法,而是用三角形面代替原来的"像素点",对其进行一系列的分割以实现最终效果.实验结果表明,方法可以实现三维图形生成算法可视化仿真效果,突破了原来只能实现二维图形的局限性,而且生成图形的速度较快,具有很好的逼真度.     

Visualizing whole-brain DTI tractography with GPU-based Tuboids and LoD management

Diffusion Tensor Imaging (DTI) of the human brain, coupled with tractography techniques, enable the extraction of large-collections of three-dimensional tract pathways per subject. These pathways and pathway bundles represent the connectivity between different brain regions and are critical for the understanding of brain related diseases. A flexible and efficient GPU-based rendering technique for DTI tractography data is presented that addresses common performance bottlenecks and image-quality issues, allowing interactive render rates to be achieved on commodity hardware. An occlusion query-based pathway LoD management system for streamlines/streamtubes/tuboids is introduced that optimizes input geometry, vertex processing, and fragment processing loads, and helps reduce overdraw. The tuboid, a fully-shaded streamtube impostor constructed entirely on the GPU from streamline vertices, is also introduced. Unlike full streamtubes and other impostor constructs, tuboids require little to no preprocessing or extra space over the original streamline data. The supported fragment processing levels of detail range from texture-based draft shading to full raycast normal computation, Phong shading, environment mapping, and curvature-correct text labeling. The presented text labeling technique for tuboids provides adaptive, aesthetically pleasing labels that appear attached to the surface of the tubes. Furthermore, an occlusion query aggregating and scheduling scheme for tuboids is described that reduces the query overhead. Results for a tractography dataset are presented, and demonstrate that LoD-managed tuboids offer benefits over traditional streamtubes both in performance and appearance.     

Visualizing Objects with Mirrors

We present an automatic camera and mirrors placement method for visualizing 3D scenes such that complete and nonredundant visibility is guaranteed, as well as highest image resolution. We provide algorithms and geometries for optimal object‐independent mirrors and viewpoint placement for all objects in a specific class. We also briefly consider object‐dependent algorithmic problems, where mirrors and viewpoints are positioned as a function of the object.     

Visualizing business data with generalized treemaps

Business data is often presented using simple business graphics. These familiar visualizations are effective for providing overviews, but fall short for the presentation of large amounts of detailed information. Treemaps can provide such detail, but are often not easy to understand. We present how standard treemap algorithms can be adapted such that the results mimic familiar business graphics. Specifically, we present the use of different layout algorithms per level, a number of variations of the squarified algorithm, the use of variable borders, and the use of non-rectangular shapes. The combined use of these leads to histograms, pie charts and a variety of other styles     

Visualizing second-order tensor fields with hyperstreamlines

A method developed to help scientists visualize 3D tensor data is presented. The method is based on the concept of a hyperstreamline, the simplest continuous tensor structure that can be extracted from a tensor field. Hyperstreamlines for a particular case of symmetric tensor fields are introduced, and a structural depiction of symmetric tensor fields is derived from the representation of many hyperstreamlines. A method for visualizing unsymmetric tensor data by encoding an additional vector field along the trajectory of the hyperstreamlines is discussed     

Visualizing with VTK: a tutorial

We introduce basic concepts behind the Visualization Toolkit (VTK). An overview of the system, plus some detailed examples, will assist in learning this system. The tutorial targets researchers of any discipline who have 2D or 3D data and want more control over the visualization process than a turn-key system can provide. It also assists developers who would like to incorporate VTK into an application as a visualization or data processing engine     

Visualizing Demographic Trajectories with Self-Organizing Maps

In recent years, the proliferation of multi-temporal census data products and the increased capabilities of geospatial analysis and visualization techniques have encouraged longitudinal analyses of socioeconomic census data. Traditional cartographic methods for illustrating socioeconomic change tend to rely either on comparison of multiple temporal snapshots or on explicit representation of the magnitude of change occurring between different time periods. This paper proposes to add another perspective to the visualization of temporal change, by linking multi-temporal observations to a geometric configuration that is not based on geographic space, but on a spatialized representation of n-dimensional attribute space. The presented methodology aims at providing a cognitively plausible representation of changes occurring inside census areas by representing their attribute space trajectories as line features traversing a two-dimensional display space. First, the self-organizing map (SOM) method is used to transform n-dimensional data such that the resulting two-dimensional configuration can be represented with standard GIS data structures. Then, individual census observations are mapped onto the neural network and linked as temporal vertices to represent attribute space trajectories as directed graphs. This method is demonstrated for a data set containing 254 counties and 32 demographic variables. Various transformations and visual results are presented and discussed in the paper, from the visualization of individual component planes and trajectory clusters to the mapping of different attributes onto temporal trajectories.     

Visualizing massively multithreaded applications with ThreadScope

As highly parallel multicore machines become commonplace, programs must exhibit more concurrency to exploit the available hardware. Many multithreaded programming models already encourage programmers to create hundreds or thousands of short‐lived threads that interact in complex ways. Programmers need to be able to analyze, tune, and troubleshoot these large‐scale multithreaded programs. To address this problem, we present ThreadScope: a tool for tracing, visualizing, and analyzing massively multithreaded programs. ThreadScope extracts the machine‐independent program structure from execution trace data from a variety of tracing tools and displays it as a graph of dependent execution blocks and memory objects, enabling identification of synchronization and structural problems, even if they did not occur in the traced run. It also uses graph‐based analysis to identify potential problems. We demonstrate the use of ThreadScope to view program structure, memory access patterns, and synchronization problems in three programming environments and seven applications. Copyright © 2009 John Wiley & Sons, Ltd.     

Visualizing Multidimensional Data with Order Statistics

Multidimensional data sets are common in many domains, and dimensionality reduction methods that determine a lower dimensional embedding are widely used for visualizing such data sets. This paper presents a novel method to project data onto a lower dimensional space by taking into account the order statistics of the individual data points, which are quantified by their depth or centrality in the overall set. Thus, in addition to conveying relative distances in the data, the proposed method also preserves the order statistics, which are often lost or misrepresented by existing visualization methods. The proposed method entails a modification of the optimization objective of conventional multidimensional scaling (MDS) by introducing a term that penalizes discrepancies between centrality structures in the original space and the embedding. We also introduce two strategies for visualizing lower dimensional embeddings of multidimensional data that takes advantage of the coherent representation of centrality provided by the proposed projection method. We demonstrate the effectiveness of our visualization with comparisons on different kinds of multidimensional data, including categorical and multimodal, from a variety of domains such as botany and health care.     

Visualizing Multidimensional Data with Glyph SPLOMs

Scatterplot matrices or SPLOMs provide a feasible method of visualizing and representing multi‐dimensional data especially for a small number of dimensions. For very high dimensional data, we introduce a novel technique to summarize a SPLOM, as a clustered matrix of glyphs, or a Glyph SPLOM. Each glyph visually encodes a general measure of dependency strength, distance correlation, and a logical dependency class based on the occupancy of the scatterplot quadrants. We present the Glyph SPLOM as a general alternative to the traditional correlation based heatmap and the scatterplot matrix in two examples: demography data from the World Health Organization (WHO), and gene expression data from developmental biology. By using both, dependency class and strength, the Glyph SPLOM illustrates high dimensional data in more detail than a heatmap but with more summarization than a SPLOM. More importantly, the summarization capabilities of Glyph SPLOM allow for the assertion of “necessity” causal relationships in the data and the reconstruction of interaction networks in various dynamic systems.     

Merging fossil specimens with computer-generated information

While dinosaur fossils reveal much, they keep us guessing about the original organisms' color, sound, and behavior. For several years, modern paleontologists have used 3D computer graphics to help reconstruct these pieces of the past. Augmented reality (AR) leverages this technology to provide an interface that enhances the real world with synthetic supplements. Paleontologists can use AR to present virtual data directly within a real, 3D environment rather than on a flat monitor. This technology's immersiveness can give paleontologists new insights into their research and communicate the results to museum visitors in an exciting and effective way.