Abstract #2838
Probabilistic Fiber Tractography Using Neighborhood Information
Helen Schomburg 1 , Thorsten Hohage 1 , Christoph Rgge 1 , Sabine Hofer 2,3 , and Jens Frahm 2
1
Institute for Numerical and Applied
Mathematics, Georg-August-Universitt Gttingen,
Gttingen, Germany,
2
Biomedizinische
NMR Forschungs GmbH, Max-Planck-Institut fr
biophysikalische Chemie, Gttingen, Germany,
3
Bernstein
Center for Computational Neuroscience, Gttingen,
Germany
We present an algorithm for probabilistic tractography
on HARDI data that exploits diffusion information of
neighboring regions. In each iteration step, a guiding
direction is determined from the previously obtained
fiber fragment. Moreover, the region ahead is explored
by computing a set of candidate directions and
corresponding weights. This procedure is repeated
recursively. The first set of candidate directions is
assigned a probability based upon the final weight
configuration. Then, a sample from this set is chosen
randomly and contributes to a new tracking direction.
The method is tested on a diffusion phantom as well as
on in vivo data.
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