Abstract
Recent advances in magnetic resonance imaging have provided methods
for the acquisition of high-resolution diffusion tensor fields.
Their 3D-visualization with streamline-based techniques--called
fiber tracking--allow analysis of cerebral white matter tracts for
diagnostic, therapeutic as well as neuro-scientific purposes. The
illusiveness of fiber visualizations and the inability to reliably
visualize branching structures are problems still waiting for solutions.
In this paper we present an on-the-fly approach to the tracking
of branching and crossing fibers by dynamically setting secondary
seeds in regions where branching is assumed, thus avoiding computationally
intensive preprocessing steps. Moreover, we propose an uncertainty
mapping technique that uses color-coding to enrich 3D fiber displays
with information on their validity. Probability values for fiber
samples are computed from dataset features as well as characteristics
of the tracking process. In contrast to data optimization and pre-processing
approaches, our algorithms focus on highly interactive visualization
scenarios in collaborative environments.
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