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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