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Improved Correspondence for DTI Population Studies Via Unbiased Atlas Building

  • Casey Goodlett
  • Brad Davis
  • Remi Jean
  • John Gilmore
  • Guido Gerig
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4191)

Abstract

We present a method for automatically finding correspondence in Diffusion Tensor Imaging (DTI) from deformable registration to a common atlas. The registration jointly produces an average DTI atlas, which is unbiased with respect to the choice of a template image, along with diffeomorphic correspondence between each image. The registration image match metric uses a feature detector for thin fiber structures of white matter, and interpolation and averaging of diffusion tensors use the Riemannian symmetric space framework. The anatomically significant correspondence provides a basis for comparison of tensor features and fiber tract geometry in clinical studies and for building DTI population atlases.

Keywords

Fractional Anisotropy Baseline Image Subject Image Deformable Registration Cumulative Histogram 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Casey Goodlett
    • 1
  • Brad Davis
    • 1
    • 2
  • Remi Jean
    • 3
  • John Gilmore
    • 3
  • Guido Gerig
    • 1
    • 3
  1. 1.Department of Computer ScienceUniversity of North Carolina 
  2. 2.Department of Radiation OncologyUniversity of North Carolina 
  3. 3.Department of PsychiatryUniversity of North Carolina 

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