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Abstract #0800

Combining HARDI Datasets With More Than One bValue Improves Diffusion MRI-Based Cortical Parcellation

Zoltan Nagy 1,2 , Tara Ganepola 3 , Martin I Sereno 3 , Nikolaus Weiskopf 1 , and Daniel C Alexander 4

1 Wellcome Trust Centre for Neuroimaging, University College London, London, United Kingdom, 2 Laboratory for Social and Neural Systems Research, University of Zrich, Zrich, Switzerland, 3 Department of Cognitive, Perceptual and Brain Sciences, University College London, United Kingdom, 4 Center for Medical Image Computing, University College London, United Kingdom

MRI based invivo histology of brain tissue is an active research area with several approaches using different contrasts. Previously, we have used high angular resolution diffusion imaging data with a single bvalue to construct a feature vector, which we proposed as a method for grey matter cortical parcellation. Here, we investigate the utility of combining data from several bvalues (i.e. constructing a 2D feature matrix). The results strongly suggest that the combining information contained in these different datasets improves the parcellation. Future work will refine the choice of bvalues and focus on histilogical validation.

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