Diffusion Tensor Visualization with Glyph Packing

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 ca...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on visualization and computer graphics 2006-09, Vol.12 (5), p.1329-1336
Main Authors: Kindlmann, G., and Westin, C.-F.
Format: Article
Language:English
Subjects:
Anisotropic magnetoresistance
anisotropic sampling
Convolution
Data visualization
Diffusion
Diffusion tensor
Diffusion tensor imaging
fiber tractography
glyphs
Laboratories
Mathematical analysis
Mathematical models
Mesh generation
particle systems
Potential energy
Sampling methods
Shape measurement
Surface layer
Tensile stress
Tensors
Texture
Visualization
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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
ISSN:1077-2626
1941-0506
DOI:10.1109/TVCG.2006.134