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A Survey of Illustrative Visualization Techniques for Diffusion-Weighted MRI Tractography

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Visualization and Processing of Higher Order Descriptors for Multi-Valued Data

Part of the book series: Mathematics and Visualization ((MATHVISUAL))

Abstract

Fiber tracking is a common method for analyzing 3D tensor fields that arise from diffusion-weighted magnetic resonance imaging. This method can visualize, e.g., the structure of the brain’s white matter or that of muscle tissue. Fiber tracking results in dense, line-based datasets that are often too large to understand when shown directly. This chapter provides a survey of recent illustrative visualization approaches that address this problem. We group this work into techniques that improve the depth perception of fiber tracts, techniques that visualize additional data about the tracts, techniques that employ focus+context visualization, visualizations of fiber tract bundles, representations of uncertainty in the context of probabilistic fiber tracking, and techniques that rely on a spatially abstracted visualization of connectivity.

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Notes

  1. 1.

    This is also true for probabilistic tracking [4], even if probabilistic tractography results—due to the size of the generated data—are typically visualized by displaying the scalar probabilities that different brain regions are connected to a seed region. In fact, these dataset sizes are one motivation to employ illustrative visualization as it promises to present the data in an understandable form.

  2. 2.

    Surveys of the use of illustrative visualization techniques for domains other than brain connectivity have been presented for flow visualization [10] and as a general tutorial/overview [63].

  3. 3.

    The chapter focuses on the visualization of brain connectivity. The discussed methods, however, can also be applied to other datasets that have similar characteristics, for example muscle fiber data.

  4. 4.

    For a comparison of simple line rendering, shaded tubes, illuminated line rendering, and illuminated line rendering with shadowing see Figure 4 in Peeters et al.’s [49] paper.

  5. 5.

    Everts et al.’s [24] approach could be viewed as an abstraction of line-based rendering with shadowing: it uses lines as the basic primitive, conveys occlusion, and does not rely on line shading.

  6. 6.

    Halos had previously already been used in computer graphics [2] and visualization [13, 64].

  7. 7.

    The example images in Fig. 2 were created with the depth-dependent halos demo; see the project website at http://tobias.isenberg.cc/VideosAndDemos/Everts2009DDH .

  8. 8.

    Ambient occlusion has also already been used in other sub-fields of visualization [62].

  9. 9.

    The example images in Figs. 1 and 4 were created with the tool OpenWalnut [19, 20]; see the website at http://www.openwalnut.org/ .

  10. 10.

    In the context of the brain connectivity visualization, Laidlaw et al. [40] have used this principle to illustratively show slices of DTI data based on inspirations from oil painting.

  11. 11.

    Everts et al. [25] extended this idea, encoding data properties in (colored) patterns for flow data.

  12. 12.

    The example images in Fig. 9 were created with the project’s demo; see the website at http://tobias.isenberg.cc/VideosAndDemos/Svetachov2010DCI .

  13. 13.

    The example images in Fig. 12 were created with the vIST/e project’s demo (using a test release); see the website at http://bmia.bmt.tue.nl/software/viste/ and the SourceForge repository at http://sourceforge.net/projects/viste/files/ .

  14. 14.

    The images in Fig. 13 were created with DTITool (provided by Ralph Brecheisen), an early version of the vIST/e software.

  15. 15.

    A downloadable demo of Jianu et al.’s [36] technique is available at http://graphics.cs.brown.edu/research/sciviz/newbraininteraction/tutorial.htm and an online demo can be found at http://graphics.cs.brown.edu/research/sciviz/newbraininteraction/BrainComplete/P3/gmap_brain.html .

  16. 16.

    The example image in Fig. 17 was created with the braingl tool, see the webpage at http://code.google.com/p/braingl/ .

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Acknowledgements

I would like to thank all of the people who provided example images, tried to find material from old sources, or referred me to others—sometimes on very short notice. In particular, I would like to thank Joachim Böttger, Silvia Born, Ralph Brecheisen, Jesús Díaz-García, Mathias Goldau, Mario Hlawitschka, Radu Jianu, Daniel F. Keefe, Mathias Schott, Thomas Schultz, Pere-Pau Vázquez, and Anna Vilanova. I also specifically wish to thank those authors who provided demo applications of their publications that I could use to create my own example visualizations. Finally, I would like to thank Kai Lawonn and Wesley Willet as well as the anonymous reviewers for their valuable comments on drafts of this survey.

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  1. Inria-Saclay, Orsay, France

    Tobias Isenberg

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  1. Linköping University, Norrköping, Sweden

    Ingrid Hotz

  2. Visualization and Medical Image Analysis, University of Bonn, Bonn, Germany

    Thomas Schultz

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Isenberg, T. (2015). A Survey of Illustrative Visualization Techniques for Diffusion-Weighted MRI Tractography. In: Hotz, I., Schultz, T. (eds) Visualization and Processing of Higher Order Descriptors for Multi-Valued Data. Mathematics and Visualization. Springer, Cham. https://doi.org/10.1007/978-3-319-15090-1_12

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