Exploration of the Brain's White Matter Structure through Visual Abstraction and Multi-Scale Local Fiber Tract Contraction

We present a visualization technique for brain fiber tracts from DTI data that provides insight into the structure of white matter through visual abstraction. We achieve this abstraction by analyzing the local similarity of tract segment directions at different scales using a stepwise increase of th...

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Bibliographic Details
Published in:IEEE transactions on visualization and computer graphics 2015-07, Vol.21 (7), p.808-821
Main Authors: Everts, Maarten H., Begue, Eric, Bekker, Henk, Roerdink, Jos B. T. M., Isenberg, Tobias
Format: Article
Language:English
Subjects:
Abstracts
Adult
Cognitive science
Computer Graphics
Computer science
Data visualization
Diffusion tensor imaging
Diffusion Tensor Imaging (DTI)
Female
fiber tracts
Humans
illustrative visualization
Image Processing, Computer-Assisted
Magnetic Resonance Imaging
Male
multi-scale representation
Nerve fibers
Neural Pathways - anatomy & histology
Tensile stress
Vectors
visual abstraction
Visualization
White Matter - anatomy & histology
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Summary:We present a visualization technique for brain fiber tracts from DTI data that provides insight into the structure of white matter through visual abstraction. We achieve this abstraction by analyzing the local similarity of tract segment directions at different scales using a stepwise increase of the search range. Next, locally similar tract segments are moved toward each other in an iterative process, resulting in a local contraction of tracts perpendicular to the local tract direction at a given scale. This not only leads to the abstraction of the global structure of the white matter as represented by the tracts, but also creates volumetric voids. This increase of empty space decreases the mutual occlusion of tracts and, consequently, results in a better understanding of the brain's three-dimensional fiber tract structure. Our implementation supports an interactive and continuous transition between the original and the abstracted representations via various scale levels of similarity. We also support the selection of groups of tracts, which are highlighted and rendered with the abstracted visualization as context.
ISSN:1077-2626
1941-0506
DOI:10.1109/TVCG.2015.2403323