Glyph-based Comparative Visualization for Diffusion Tensor Fields

Changgong Zhang, Thomas Schultz, Kai Lawonn, Elmar Eisemann und Anna Vilanova
In: IEEE Trans. on Visualization and Computer Graphics (2016), 22:1(797-806)
 

Abstract

Diffusion Tensor Imaging (DTI) is a magnetic resonance imaging modality that enables the in-vivo reconstruction and visualization of fibrous structures. To inspect the local and individual diffusion tensors, expressed as 3 × 3 symmetric and positive-definite matrices, glyph-based visualizations are commonly used since they are able to effectively convey full aspects of the diffusion tensor. For several applications, it is necessary to compare tensor fields, e.g., to study the effects of acquisition parameters, or to investigate the influence of pathologies on white matter structures. The comparison is commonly done by extracting scalar information out of the tensor fields and then comparing these scalar fields, which leads to a loss of information. If the glyph representation is kept, juxtaposition or superposition can be used, but neither facilitates the identification and interpretation of the differences between the tensor fields. Inspired by the checkerboard-style visualization and the superquadric tensor glyph, we designed a new glyph to locally visualize differences between two diffusion tensors by combining juxtaposition and explicit encoding. The new glyphs allow us to efficiently and effectively identify the diffusion tensor (a) scale, (b) anisotropy type, and (c) orientation differences as demonstrated in a user study. Tensor scale, anisotropy and orientation are related to anatomical information that is relevant in most DTI applications. We applied our glyphs to investigate the differences between two DTI datasets of the human brain acquired with different b-values, and from a healthy subject and a HIV-infected subject respectively.

Stichwörter: Comparative Visualization, Diffusion Tensor Field, Glyph Design

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Bibtex

@ARTICLE{Zhang2015Vis,
    author = {Zhang, Changgong and Schultz, Thomas and Lawonn, Kai and Eisemann, Elmar and Vilanova, Anna},
     pages = {797--806},
     title = {Glyph-based Comparative Visualization for Diffusion Tensor Fields},
   journal = {IEEE Trans. on Visualization and Computer Graphics},
    volume = {22},
    number = {1},
      year = {2016},
  keywords = {Comparative Visualization, Diffusion Tensor Field, Glyph Design},
  abstract = {Diffusion Tensor Imaging (DTI) is a magnetic resonance imaging modality that enables the in-vivo
              reconstruction and
              visualization of fibrous structures. To inspect the local and individual diffusion tensors,
              expressed as 3 × 3 symmetric and positive-definite matrices, glyph-based visualizations are
              commonly used since they are able to effectively convey full aspects of the diffusion tensor. For
              several applications, it is necessary to compare tensor fields, e.g., to study the effects of
              acquisition parameters, or to investigate the influence of pathologies on white matter structures.
              The comparison is commonly done by extracting scalar information out of the tensor fields and then
              comparing these scalar fields, which leads to a loss of information. If the glyph representation is
              kept, juxtaposition or superposition can be used, but neither facilitates the identification and
              interpretation of the differences between the tensor fields. Inspired by the checkerboard-style
              visualization and the superquadric tensor glyph, we designed a new glyph to locally visualize
              differences between two diffusion tensors by combining juxtaposition and explicit encoding. The new
              glyphs allow us to
              efficiently and effectively identify the diffusion tensor (a) scale, (b) anisotropy type, and (c)
              orientation differences as demonstrated in a user study. Tensor scale, anisotropy and orientation
              are related to anatomical information that is relevant in most DTI applications. We applied our
              glyphs to investigate the differences between two DTI datasets of the human brain acquired with
              different b-values, and from a healthy subject and a HIV-infected subject respectively.}
}