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Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009

Volume 5761 of the series Lecture Notes in Computer Science pp 886-893

Quantifying Brain Connectivity: A Comparative Tractography Study

  • Ting-Shuo YoAffiliated withCarnegie Mellon UniversityMax Planck Institute for Human Cognitive and Brain Sciences
  • , Alfred AnwanderAffiliated withCarnegie Mellon UniversityMax Planck Institute for Human Cognitive and Brain Sciences
  • , Maxime DescoteauxAffiliated withCarnegie Mellon UniversityNeurospin / CEA Saclay, Gif-sur-Yvette
  • , Pierre FillardAffiliated withCarnegie Mellon UniversityNeurospin / CEA Saclay, Gif-sur-Yvette
  • , Cyril PouponAffiliated withCarnegie Mellon UniversityNeurospin / CEA Saclay, Gif-sur-Yvette
  • , T. R. KnöscheAffiliated withCarnegie Mellon UniversityMax Planck Institute for Human Cognitive and Brain Sciences

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Abstract

In this paper, we compare a representative selection of current state-of-the-art algorithms in diffusion-weighted magnetic resonance imaging (dwMRI) tractography, and propose a novel way to quantitatively define the connectivity between brain regions. As criterion for the comparison, we quantify the connectivity computed with the different methods. We provide initial results using diffusion tensor, spherical deconvolution, ball-and-stick model, and persistent angular structure (PAS) along with deterministic and probabilistic tractography algorithms on a human DWI dataset. The connectivity is presented for a representative selection of regions in the brain in matrices and connectograms.Our results show that fiber crossing models are able to reveal connections between more brain areas than the simple tensor model. Probabilistic approaches show in average more connected regions but lower connectivity values than deterministic methods.