Fast and resolution independent line integral convolution
SIGGRAPH '95 Proceedings of the 22nd annual conference on Computer graphics and interactive techniques
Interactive visualization of 3D-vector fields using illuminated stream lines
Proceedings of the 7th conference on Visualization '96
Tensorlines: advection-diffusion based propagation through diffusion tensor fields
VIS '99 Proceedings of the conference on Visualization '99: celebrating ten years
Strategies for Direct Volume Rendering of Diffusion Tensor Fields
IEEE Transactions on Visualization and Computer Graphics
Visualizing Diffusion Tensor MR Images Using Streamtubes and Streamsurfaces
IEEE Transactions on Visualization and Computer Graphics
Point-Based Probabilistic Surfaces to Show Surface Uncertainty
IEEE Transactions on Visualization and Computer Graphics
DTI visualization with streamsurfaces and evenly-spaced volume seeding
VISSYM'04 Proceedings of the Sixth Joint Eurographics - IEEE TCVG conference on Visualization
Diffusion MRI Tractography of Crossing Fibers by Cone-Beam ODF Regularization
Proceedings of the 31st DAGM Symposium on Pattern Recognition
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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.