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<div class="moz-text-html" lang="x-unicode"> A T T E N T I O N:
Please note the room being different than usual.<br>
<br>
<br>
<br>
I N V I T A T I O N<br>
<div class="moz-forward-container"><font face="sans-serif"> <br>
<br>
to the Research Seminar 'Computer Graphics, Image Processing,
and Visualization'<br>
<br>
</font><font face="sans-serif"> on Wednesday<font
face="sans-serif">, September 9th, 2015, at 1:15 PM,<br>
in Room P-702 in the Paulinum, Augustusplatz.</font><br>
</font><br>
<br>
The talk is given by<br>
<br>
André Reichenbach<br>
Image and Signal Processing Group<br>
Institute of Computer Science<br>
Leipzig University<br>
<br>
and is entitled<br>
<br>
"Fiber Stipples for Crossing Tracts in Probabilistic
Tractography".<br>
<br>
Abstract:<br>
<br>
Given diffusion weighted magnetic resonance (dMRI) data,
tractography<br>
methods may reconstruct estimations of neural connections of
the human<br>
brain, so called tractograms. Probabilistic tractography
algorithms<br>
generate a scalar value for each point of the brain, which
describes the<br>
confidence of an existing structural connection to a
predefined seed<br>
region. Recently presented Fiber-Stippling is a promising
tool to<br>
effectively visualize such scalar values on axis aligned
cutting planes.<br>
However, Fiber-Stippling only works with principal diffusion
directions and<br>
cannot handle complex tract configurations, such as
overlapping or crossing<br>
tracts, which are very important to neuroscience. In this
talk I will<br>
present an illustrative technique for probabilistic tracts
in such<br>
configurations, which is based on Fiber-Stippling. This
technique supports<br>
multiple diffusion directions as given by high angular
resolution<br>
diffusion images (HARDI) and hence can visualize crossing
tracts, while<br>
preserving all of the advantages of Fiber-Stippling.<font
face="sans-serif"><br>
<br>
<br>
On behalf of Professor Scheuermann all those interested are
cordially invited to attend.<br>
<br>
<br>
Yours sincerely,</font><br>
<br>
Tom Liebmann<br>
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