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. 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 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.

 Best Paper Award

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@inproceedings{ bib:2017_vcbm_diftensgroup,
    author = {Changgong Zhang and Thomas H{\"o}llt and Matthan Caan and Elmar Eisemann and Anna Vilanova},
    title = { Comparative Visualization for Diffusion Tensor Imaging Group Study at Multiple Levels of Detail },
    booktitle = { Proceedings of Visual Computing for Biology and Medicine (VCBM) },
    year = { 2017 },
    doi = { 10.2312/vcbm.20171237 },
}