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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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