Glyph-based comparative visualization for diffusion tensor fields

Changgong Zhang, Thomas Schultz, Kai Lawonn, Elmar Eisemann, Anna Vilanova

Research output: Contribution to journalArticleAcademicpeer-review

38 Citations (Scopus)


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, 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. This 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, simple juxtaposition or superposition can be used. However, 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 design a new glyph to locally visualize differences between two diffusion tensors by combining juxtaposition and explicit encoding. Because tensor scale, anisotropy type, and orientation are related to anatomical information relevant for DTI applications, we focus on visualizing tensor differences in these three aspects. As demonstrated in a user study, our new glyph design allows users to efficiently and effectively identify the tensor differences. We also apply our new glyphs to investigate the differences between DTI datasets of the human brain in two different contexts using different b-values, and to compare datasets from a healthy and HIV-infected subject.

Original languageEnglish
Article number7192722
Pages (from-to)797-806
Number of pages10
JournalIEEE Transactions on Visualization and Computer Graphics
Issue number1
Publication statusPublished - 31 Jan 2016


  • Anisotropic magnetoresistance
  • Data visualization
  • Diffusion tensor imaging
  • Encoding
  • Shape
  • Tensile stress
  • Visualization


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