Information-theoretic analysis of brain white matter fiber orientation distribution functions

Authors:
Ming-Chang Chiang;Andrea D. Klunder;Katie McMahon;Greig I. De Zubicaray;Margaret J. Wright;Arthur W. Toga;Paul M. Thompson
Affiliations:
Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Los Angeles, CA;Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Los Angeles, CA;Centre for Magnetic Resonance, University of Queensland, Brisbane, Queensland, Australia;Centre for Magnetic Resonance, University of Queensland, Brisbane, Queensland, Australia;Genetic Epidemiology Lab, Queensland Institute of Medical Research, PO Royal Brisbane Hospital, Queensland, Australia;Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Los Angeles, CA;Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Los Angeles, CA
Venue:
IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
Year:
2007

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Abstract

We propose a new information-theoretic metric, the symmetric Kullback-Leibler divergence (sKL-divergence), to measure the difference between two water diffusivity profiles in high angular resolution diffusion imaging (HARDI). Water diffusivity profiles are modeled as probability density functions on the unit sphere, and the sKL-divergence is computed from a spherical harmonic series, which greatly reduces computational complexity. Adjustment of the orientation of diffusivity functions is essential when the image is being warped, so we propose a fast algorithm to determine the principal direction of diffusivity functions using principal component analysis (PCA). We compare sKL-divergence with other inner-product based cost functions using synthetic samples and real HARDI data, and show that the sKL-divergence is highly sensitive in detecting small differences between two diffusivity profiles and therefore shows promise for applications in the nonlinear registration and multisubject statistical analysis of HARDI data.