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Abstract

Topological methods give concise and expressive visual
representations of flow fields. The present work suggests a
comparable method for the visualization of human brain diffusion MRI
data. We explore existing techniques for the topological analysis of
generic tensor fields, but find them inappropriate for diffusion MRI
data. Thus, we propose a novel approach that considers the
asymptotic behavior of a probabilistic fiber tracking method and
define analogs of the basic concepts of flow topology, like critical
points, basins, and faces, with interpretations in terms of brain
anatomy. The resulting features are fuzzy, reflecting the
uncertainty inherent in any connectivity estimate from diffusion
imaging. We describe an algorithm to extract the new type of
features, demonstrate its robustness under noise, and present
results for two regions in a diffusion MRI dataset to illustrate
that the method allows a meaningful visual analysis of probabilistic
fiber tracking results.