Diffusion Tensor Visualization with Glyph Packing


G. Kindlmann, C.-F. Westin
IEEE Transactions on Visualization and Computer Graphics (Proceedings Visualization / Information Visualization 2006)
Volume 12, Number 5, Pages 1329-1335
September-October, 2006

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Abstract

A common goal of multivariate visualization is to enable data inspection at discrete points, while also illustrating larger-scale continuous structures. In diffusion tensor visualization, glyphs are typically used to meet the first goal, and methods such as texture synthesis or fiber tractography can address the second. We adapt particle systems originally developed for surface modeling and anisotropic mesh generation to enhance the utility of glyph-based tensor visualizations. By carefully distributing glyphs throughout the field (either on a slice, or in the volume) into a dense packing, using potential energy profiles shaped by the local tensor value, we remove undue visual emphasis of the regular sampling grid of the data, and the underlying continuous features become more apparent. The method is demonstrated on a DT-MRI scan of a patient with a brain tumor.

Glyph packing result in 2D
Glyph packing produces a dense texture-like arrangement of diffusion tensor superquadric glyphs on an axial slice through the left posterior region of a human brain.


Reference

Kindlmann G, Westin CF. Diffusion tensor visualization with glyph packing. IEEE Transactions on Visualization and Computer Graphics (Proceedings Visualization / Information Visualization 2006) 2006;12(5):1329-1335.

Bibtex entry

@article{KindlmannTVCG2006,
  author         = {Gordon Kindlmann and Carl-Fredrik Westin},                 
  title          = {Diffusion Tensor Visualization with Glyph Packing},        
  year           = {2006},                                                     
  month          = {September-October},                                        
  journal        = {IEEE Transactions on Visualization and Computer Graphics   
                   (Proceedings Visualization / Information Visualization      
                   2006)},                                                     
  volume         = {12},                                                       
  number         = {5},                                                        
  pages          = {1329--1335}
}                                              

Grants

NIH T32-EB002177, NIH P41-RR13218 (NAC), NIH U41-RR019703, Brain Science Foundation

Research area

DTI

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