Abstract
We apply an information-theoretic cost metric, the symmetrized Kullback-Leibler (sKL) divergence, or $J$-divergence, to fluid registration of diffusion tensor images. The difference between diffusion tensors is quantified based on the sKL-divergence of their associated probability density functions (PDFs). Three-dimensional DTI data from 34 subjects were fluidly registered to an optimized target image. To allow large image deformations but preserve image topology, we regularized the flow with a large-deformation diffeomorphic mapping based on the kinematics of a Navier-Stokes fluid. A driving force was developed to minimize the $J$-divergence between the deforming source and target diffusion functions, while reorienting the flowing tensors to preserve fiber topography. In initial experiments, we showed that the sKL-divergence based on full diffusion PDFs is adaptable to higher-order diffusion models, such as high angular resolution diffusion imaging (HARDI). 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.
Original language | English |
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Article number | 4359066 |
Pages (from-to) | 442-456 |
Number of pages | 15 |
Journal | IEEE Transactions on Medical Imaging |
Volume | 27 |
Issue number | 4 |
DOIs | |
State | Published - Apr 2008 |
Externally published | Yes |
Keywords
- Diffusion tensor imaging
- Diffusion tensor imaging (DTI)
- Fluid registration
- High angular resolution diffusion imaging
- High angular resolution diffusion imaging (HARDI)
- Kullback-Leibler divergence