多细节层次扩散张量成像组研究的比较可视化

Changgong Zhang, T. Höllt, M. Caan, E. Eisemann, A. Vilanova
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引用次数: 5

摘要

扩散张量成像(DTI)组研究往往需要比较两组三维扩散张量场。研究中涉及的数据集的总数和扩散张量的多变量性质共同使这一过程具有挑战性。传统的方法是将六维扩散张量简化为一些标量,这些标量可以用单变量统计方法进行分析,并用切片视图等标准技术进行可视化。然而,由于信息减少,这仅仅提供了整个故事的一部分。如果考虑到全张量信息,可用的方法很少,而且它们侧重于对单个组的分析,而不是两组的比较。用简单的并置或叠加的方法同时比较两组扩散张量场是不切实际的。在这项工作中,我们扩展了Zhang等人[ZCH* 17]之前的工作,以直观地比较两组扩散张量场。为了处理丰富的信息,在多个细节层次上进行比较。在三维空间域中,我们提出了一种细节按需的符号表示,以支持张量集合汇总信息的渐进视觉比较。空间视图引导分析人员选择感兴趣的体素。然后在细节层面,根据张量的内在属性比较各自的原始张量集合,特别注意减少视觉混乱。我们通过比较对照组和HIV阳性患者组来证明我们的视觉分析系统的实用性。
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Comparative Visualization for Diffusion Tensor Imaging Group Study at Multiple Levels of Detail
Diffusion Tensor Imaging (DTI) group studies often require the comparison of two groups of 3D diffusion tensor fields. The total number of datasets involved in the study and the multivariate nature of diffusion tensors together make this a challenging process. The traditional approach is to reduce the six-dimensional diffusion tensor to some scalar quantities, which can be analyzed with univariate statistical methods, and visualized with standard techniques such as slice views. However, this provides merely part of the whole story due to information reduction. If to take the full tensor information into account, only few methods are available, and they focus on the analysis of a single group, rather than the comparison of two groups. Simultaneously comparing two groups of diffusion tensor fields by simple juxtaposition or superposition is rather impractical. In this work, we extend previous work by Zhang et al. [ZCH* 17] to visually compare two groups of diffusion tensor fields. To deal with the wealth of information, the comparison is carried out at multiple levels of detail. In the 3D spatial domain, we propose a details-on-demand glyph representation to support the visual comparison of the tensor ensemble summary information in a progressive manner. The spatial view guides analysts to select voxels of interest. Then at the detail level, the respective original tensor ensembles are compared in terms of tensor intrinsic properties, with special care taken to reduce visual clutter. We demonstrate the usefulness of our visual analysis system by comparing a control group and an HIV positive patient group.
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