Diffeomorphic Metric Mapping of High Angular Resolution Diffusion Imaging Based on Riemannian Structure of Orientation Distribution Functions

@article{Du2012DiffeomorphicMM,
  title={Diffeomorphic Metric Mapping of High Angular Resolution Diffusion Imaging Based on Riemannian Structure of Orientation Distribution Functions},
  author={Jia Du and Alvina Goh and Anqi Qiu},
  journal={IEEE Transactions on Medical Imaging},
  year={2012},
  volume={31},
  pages={1021-1033}
}
  • Jia Du, A. Goh, A. Qiu
  • Published 24 July 2011
  • Computer Science, Medicine, Mathematics
  • IEEE Transactions on Medical Imaging
In this paper, we propose a novel large deformation diffeomorphic registration algorithm to align high angular resolution diffusion images (HARDI) characterized by orientation distribution functions (ODFs). Our proposed algorithm seeks an optimal diffeomorphism of large deformation between two ODF fields in a spatial volume domain and at the same time, locally reorients an ODF in a manner such that it remains consistent with the surrounding anatomical structure. To this end, we first review the… 
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Large Deformation Diffeomorphic Metric Mapping of Orientation Distribution Functions
TLDR
A novel large deformation diffeomorphic registration algorithm to align high angular resolution diffusion images (HARDI) characterized by Orientation Distribution Functions (ODF) and derives the gradient of the cost function in both Riemannian spaces of diffeomorphisms and the generalized ODFs.
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A state of the art Riemannian framework for ODF computing based on Information Geometry and sparse representation of orthonormal bases and a novel scalar measurement, named Geometric Anisotropy (GA), which is the RiemANNian geodesic distance between the ODF and the isotropic ODF.
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It is shown that various orientation PDF processing operations, such as filtering, interpolation, averaging and principal geodesic analysis, may be posed as optimization problems on the Hilbert sphere, and can be solved using Riemannian gradient descent.
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High angular resolution diffusion imaging has become an important magnetic resonance technique for in vivo imaging. Most current research in this field focuses on developing methods for computing the
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This paper introduces a novel large deformation diffeomorphic metric mapping algorithm for whole brain registration where sulcal and gyral curves, cortical surfaces, and intensity images are
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TLDR
Results show that the diffeomorphic registration improved the affine alignment, and registration using SHs with higher order SHs further improved the registration accuracy by reducing the shape difference and improving the directional consistency of the registered and reference ODF maps.
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The sKL-divergence was sensitive to subtle differences between two diffusivity profiles, showing promise for nonlinear registration applications and multisubject statistical analysis of HARDI data.
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