# Visualizing Uncertainty in HARDI Tractography Using Superquadric Streamtubes

## Abstract

Standard streamtubes for the visualization of diffusion MRI data are rendered either with a circular or with an elliptic cross section whose aspect ratio indicates the relative magnitudes of the medium and minor eigenvalues. Inspired by superquadric tensor glyphs, we propose to render streamtubes with a superquadric cross section, which develops sharp edges to more clearly convey the orientation of the second and third eigenvectors where they are uniquely defined, while maintaining a circular shape when the smaller two eigenvalues are equal. As a second contribution, we apply our novel superquadric streamtubes to visualize uncertainty in the tracking direction of HARDI tractography, which we represent using a novel propagation uncertainty tensor.

## Bilder

## Zusätzliches Material

- Preprint (The definite version is available at diglib.eg.org)
*(PDF-Dokument, 2.0 MB)*

## Bibtex

@INPROCEEDINGS{Wiens:EuroVis2014, author = {Wiens, Vitalis and Schlaffke, Lara and Schmidt-Wilcke, Tobias and Schultz, Thomas}, title = {Visualizing Uncertainty in HARDI Tractography Using Superquadric Streamtubes}, booktitle = {Eurographics Conference on Visualization (EuroVis) Short Papers}, year = {2014}, month = jun, abstract = {Standard streamtubes for the visualization of diffusion MRI data are rendered either with a circular or with an elliptic cross section whose aspect ratio indicates the relative magnitudes of the medium and minor eigenvalues. Inspired by superquadric tensor glyphs, we propose to render streamtubes with a superquadric cross section, which develops sharp edges to more clearly convey the orientation of the second and third eigenvectors where they are uniquely defined, while maintaining a circular shape when the smaller two eigenvalues are equal. As a second contribution, we apply our novel superquadric streamtubes to visualize uncertainty in the tracking direction of HARDI tractography, which we represent using a novel propagation uncertainty tensor.} }