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Abstract #4687

Strategies for Building a Morphologically Faithful Average Brain Template from Population Diffusion MRI Data

Mustafa Okan Irfanoglu1,2,3, Neda Sadeghi2, Amritha Nayak1,2,3, and Carlo Pierpaoli1,2

1Quantitative Medical Imaging Section, NIBIB/NIH, Bethesda, MD, United States, 2SQUITS/NICHD/NIH, Bethesda, MD, United States, 3Henry Jackson Foundation, Bethesda, MD, United States

The availability of anatomically accurate MRI atlases, which can be used as target templates for registration, is essential for quantitative analysis of MRI data. Ideally the computed atlas should be representative of the average features of the population at each voxel location. In this work, we investigate the ability of the most common atlasing approach (i.e. iterative registration followed by averaging) of assuring morphological accuracy. We also evaluate whether constraining the individual deformation fields with deformation-based information helps achieving this goal. We perform our atlas creation tests from full DTI data, using a novel diffusion tensor based diffeomorphic registration technique. We conclude that current atlasing techniques lead to templates that do not faithfully represent the average morphology of the population but that by applying appropriate constraints significant improvements toward this goal can be achieved.

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