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

Chiang, Ming-Chang, Klunder, Andrea D., McMahon, Katie L., De Zubicaray, Greig I., Wright, Margaret J., Toga, Arthur W. and Thompson, Paul M. (2007). Information-theoretic analysis of brain white matter fiber orientation distribution functions. In: Proceedings: Information Processing in Medical Imaging. 20th International Conference on Information Processing in Medical Imaging, Kerkrade, The Netherlands, (172-182). 2-6 July 2007. doi:10.1007/978-3-540-73273-0


Author Chiang, Ming-Chang
Klunder, Andrea D.
McMahon, Katie L.
De Zubicaray, Greig I.
Wright, Margaret J.
Toga, Arthur W.
Thompson, Paul M.
Title of paper Information-theoretic analysis of brain white matter fiber orientation distribution functions
Conference name 20th International Conference on Information Processing in Medical Imaging
Conference location Kerkrade, The Netherlands
Conference dates 2-6 July 2007
Proceedings title Proceedings: Information Processing in Medical Imaging   Check publisher's open access policy
Journal name Lecture Notes in Computer Science   Check publisher's open access policy
Place of Publication Berlin, Germany
Publisher Springer
Publication Year 2007
Sub-type Fully published paper
DOI 10.1007/978-3-540-73273-0
ISBN 9783540732723
3540732721
ISSN 0302-9743
1611-3349
Volume 4584
Start page 172
End page 182
Total pages 11
Language eng
Abstract/Summary 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.
Subjects 110320 Radiology and Organ Imaging
110903 Central Nervous System
Keyword High angular resolution diffusion imaging (HARDI)
Symmetric Kullback-Leibler divergence
Information-theoretic analysis
Brain
White matter fiber
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status UQ

 
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Created: Fri, 11 Dec 2009, 11:34:51 EST by Michael Affleck on behalf of Faculty of Science