Illustrative White Matter Fiber Bundles

Diffusion Tensor Imaging (DTI) has made feasible the visualization of the fibrous structure of the brain white matter. In the last decades, several fiber‐tracking methods have been developed to reconstruct the fiber tracts from DTI data. Usually these fiber tracts are shown individually based on som...

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Bibliographic Details
Published in:Computer graphics forum Vol. 29; no. 3; pp. 1013 - 1022
Main Authors: Otten, Ron, Vilanova, Anna, Van De Wetering, Huub
Format: Journal Article
Language:English
Published: Oxford, UK Blackwell Publishing Ltd 01.06.2010
Subjects:
Computer graphics
Display algorithms
I.3.3 [Computer Graphics]: Picture/Image Generation-Bitmap and framebuffer operations
I.3.3 [Computer Graphics]: Picture/Image Generation—Bitmap and framebuffer operations, Display algorithms, Viewing algorithms
Real time
Studies
Viewing algorithms
Visualization
ISSN:0167-7055, 1467-8659
Online Access:Get full text
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Summary:Diffusion Tensor Imaging (DTI) has made feasible the visualization of the fibrous structure of the brain white matter. In the last decades, several fiber‐tracking methods have been developed to reconstruct the fiber tracts from DTI data. Usually these fiber tracts are shown individually based on some selection criteria like region of interest. However, if the white matter as a whole is being visualized clutter is generated by directly rendering the individual fiber tracts. Often users are actually interested in fiber bundles, anatomically meaningful entities that from the fibers they contain. Several clustering techniques have been developed that try to group the fiber tracts in fiber bundles. However, even if clustering succeeds, the complex nature of white matter still makes it difficult to investigate. In this paper, we propose the use of illustration techniques to ease the exploration of white matter clusters. We create a technique to visualize an individual cluster as a whole. The amount of fibers visualized for the cluster is reduced to just a few hint lines, and silhouette and contours are used to improve the definition of the cluster borders. Multiple clusters can be easily visualized by a combination of the single cluster visualizations. Focus+context concepts are used to extend the multiple‐cluster renderings. Exploded views ease the exploration of the focus cluster while keeping the context clusters in an form. Real‐time results are achieved by the GPU implementation of the presented techniques.
Bibliography:istex:A068DB17C4228ABE08B89E5575FB079B6F47F07E
ArticleID:CGF1688
ark:/67375/WNG-R2M92HPV-W
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ISSN:0167-7055
1467-8659
DOI:10.1111/j.1467-8659.2009.01688.x