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Visualization of Diffusion Propagator and Multiple Parameter Diffusion Signal

  • Olivier Vaillancourt
  • Maxime Chamberland
  • Jean-Christophe Houde
  • Maxime DescoteauxEmail author
Part of the Mathematics and Visualization book series (MATHVISUAL)

Abstract

New advances in MRI technology allow the acquisition of high resolution diffusion-weighted datasets for multiple parameters such as multiple q-values, multiple b-values, multiple orientations and multiple diffusion times. These new and demanding acquisitions go beyond classical diffusion tensor imaging (DTI) and single b-value high angular resolution diffusion imaging (HARDI) acquisitions. Recent studies show that such multiple parameter diffusion can be used to infer axonal diameter distribution and other biophysical features of the white matter, otherwise not possible. Hence, this calls for novel visualization techniques to interact with such complex high-dimensional and high-resolution datasets. To date, there are no existing visualization techniques to visualize full brain images or fields of diffusion signal profiles and diffusion propagators reconstructed from them. It is important to be able to scroll in these images beyond single voxels, just as one would navigate in a whole brain map of fractional anisotropy extracted from DTI. In this chapter, we give a review of the existing visualization techniques for the local diffusion phenomenon and propose alternative visualization techniques for fields of high-dimensional 3D diffusion profiles. We introduce: (i) a volume rendering approach and (ii) a diffusion propagator silhouette glyph as a complement to existing DTI and HARDI visualization techniques. We show that these visualization techniques allow the real-time exploration of high-dimensional multi-b-value and multi-direction data such as diffusion spectrum imaging (DSI). Our visualization technique therefore opens new perspectives for 3D diffusion MRI visualization and interaction.

Keywords

Diffusion Tensor Image Graphic Processing Unit Visualization Technique Volume Rendering Orientation Distribution Function 
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.

Notes

Acknowledgements

We would like to thank our funding agencies, NSERC, MDEIE and CFI in Canada. Also, a special thanks to Michele Bosi for the open-source visualization library. Finally, thanks to Guillaume Gilbert from Philips Healthcare, MR Clinical Science, for the DSI datasets.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Olivier Vaillancourt
    • 1
  • Maxime Chamberland
    • 1
  • Jean-Christophe Houde
    • 1
  • Maxime Descoteaux
    • 1
    Email author
  1. 1.Computer Science Department, Sherbrooke Connectivity Imaging Lab (SCIL), Sherbrooke Molecular Imaging Center (CIMS)Université de SherbrookeSherbrookeCanada

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