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Abstract #1588

A Novel Strategy For Morphologically Faithful Registration and Template Creation for Diffusion MRI Data

M. Okan Irfanoglu1, Neda Sadeghi1, Carlo Pierpaoli1, and Moebius Syndrome Research Consortium2

1QMI/NIBIB, National Institutes of Health, Bethesda, MD, United States, 2National Institutes of Health, Bethesda, MD, United States

Spatial alignment of diffusion tensor MRI (DTI) data is of fundamental importance for voxelwise statistical analysis and creation of population specific atlases of diffusion MRI metrics. Most available DTI-based spatial normalization algorithms emphasize alignment of anisotropic structures and disregard the quality of alignment for gray matter and CSF-filled regions. Additionally, standard atlas creation strategies using these registration tools do not generate templates that are morphologically representative of average features of the population. In this work, we propose a new DTI-based registration and atlas creation method that aims to overcome these challenges.

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