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Abstract #3968

Non-Parametric Deformable Registration of High Angular Resolution Diffusion Data Using Diffusion Profile Statistics

Pew-Thian Yap1, Yasheng Chen1, Hongyu An1, John H. Gilmore2, Weili Lin1, Dinggang Shen1

1Department of Radiology, University of North Carolina, Chapel Hill, NC, United States; 2Department of Psychiatry, University of North Carolina, Chapel Hill, NC, United States

We propose a full-brain multi-scale feature-based deformable registration algorithm based on the statistics of the diffusion profile of HARDI data. Besides the advantage of avoiding any predetermined models which may not necessarily fit the data, our method registers the diffusion weighted images (DWIs) and allows model fitting after the registration. This essentially means that our method can be utilized as a preprocessing step for a wide assortment of available diffusion models. Our method is also well suited for clinical applications due to its low computational cost around 5 minutes on a 2.8GHz Linux machine (without algorithm optimization) to register a pair of images of typical size 128 x 128 x 80. The main idea involves extraction of statistical features directly from the diffusion profile, which includes mean diffusivity, diffusion anisotropy, regional diffusion statistics, and statistic-map-based edges.