Abstract
Neuroscientists use visualizations of diffusion data to analyze neural tracts of the brain. More specifically, probabilistic tractography algorithms are a group of methods that reconstruct tract information in diffusion data and need proper visualization. One problem neuroscientists are facing with probabilistic data is putting this information into context. Neuroscience experts already successfully utilized several techniques together with structural MRI to detect neural tracts in the living human brain which were previously only known from tracer studies in macaque monkeys. Whereas the combination with structural MRI, i.e., T1 and T2 images, has been important for these studies, new challenges ask for an integration of other imaging modalities. First, we provide an overview of the currently used visualization techniques. Then, we show how probabilistic tractography can be combined with other techniques, trying to find new and useful visualizations for multi-modal data.
Keywords
- Fractional Anisotropy
- Diffusion Tensor Imaging
- Visualization Technique
- Structural Magnetic Resonance Imaging
- Probabilistic Tract
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Acknowledgements
The authors thank the OpenWalnut project [18] for providing the software framework supporting neuroscience visualization and for distributing the Fiber Stipple source code. The software platform was used for conducting this study. We thank C. Heine and A. Wiebel for the fruitful discussions and for commenting on drafts of this article. Last but not least we want to thank the reviewers which gave us very helpful comments and advises.
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Goldau, M., Hlawitschka, M. (2016). Multi-Modal Visualization of Probabilistic Tractography. In: Linsen, L., Hamann, B., Hege, HC. (eds) Visualization in Medicine and Life Sciences III. Mathematics and Visualization. Springer, Cham. https://doi.org/10.1007/978-3-319-24523-2_9
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