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Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
09:33

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Published on: July 28, 2013

Fluid registration of diffusion tensor images using information theory.

M C Chiang1, A D Leow, A D Klunder

  • 1Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Los Angeles, CA 90095 USA.

IEEE Transactions on Medical Imaging
|April 9, 2008
PubMed
Summary

We developed a new fluid registration method using the J-divergence to accurately align diffusion tensor images. This technique preserves fiber orientation and shows promise for analyzing complex diffusion imaging data.

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Area of Science:

  • Medical Imaging
  • Computational Neuroscience
  • Information Theory

Background:

  • Diffusion Tensor Imaging (DTI) is crucial for analyzing white matter structure.
  • Accurate registration of DTI data is challenging due to large deformations and preserving fiber orientation.
  • Existing registration methods may not adequately capture subtle differences in diffusion profiles.

Purpose of the Study:

  • To introduce and evaluate an information-theoretic cost metric, the symmetrized Kullback-Leibler (sKL) divergence (J-divergence), for fluid registration of diffusion tensor images.
  • To develop a registration method that allows large deformations while preserving image topology and fiber orientation.
  • To assess the adaptability of the sKL-divergence to higher-order diffusion models like HARDI.

Main Methods:

  • Applied the J-divergence to quantify differences between diffusion tensors based on their probability density functions (PDFs).
  • Utilized a large-deformation diffeomorphic mapping, regularized by Navier-Stokes fluid kinematics, for fluid registration of 3D DTI data from 34 subjects.
  • Developed a driving force to minimize J-divergence and reorient tensors, preserving fiber topography during registration.

Main Results:

  • Demonstrated the adaptability of sKL-divergence based on full diffusion PDFs to higher-order diffusion models, including High Angular Resolution Diffusion Imaging (HARDI).
  • Showed that sKL-divergence is sensitive to subtle differences between diffusivity profiles.
  • Initial experiments confirmed the method's potential for nonlinear registration and multisubject statistical analysis of HARDI data.

Conclusions:

  • The J-divergence offers a robust metric for fluid registration of diffusion tensor images.
  • The proposed method effectively handles large deformations and preserves fiber orientation, crucial for accurate neuroimaging analysis.
  • This approach shows significant promise for advanced diffusion imaging analyses, including HARDI and multisubject studies.