Rank-2 model-order selection in diffusion tensor MRI: Infromation complexity based on the total Kullback-Leibler divergence

Jianfei Yang*, Dirk H.J. Poot, Matthan W.A. Caan, Frans M. Vos, Lucas J. Van Vliet

*Corresponding author for this work

Research output: Contribution to journalConference articleAcademicpeer-review

1 Citation (Scopus)

Abstract

Diffusion-weighted MRI (DW-MRI) can assess the integrity of white matter (WM) structures in the human brain. Multi-compartment analysis of DW-MRI requires an estimate of the number of compartments to permit unbiased estimation of the diffusion shape in a single fibers as well as crossing fascicles. We propose a new, rotation-invariant measure to assess the suitability of a model by a measure for information complexity (ICOMP) based on the total Kullback-Leibler divergence (TKLD). ICOMP-TKLD is evaluated on simulated data and on data from the Human Connectome Project. Compared to the state-of-the-art, ICOMP-TKLD is the only method that yields reliable model-order selection in both homogeneous and heterogeneous WM regions. Therefore, ICOM-TKLD may open the way for structure-adaptive estimation of diffusion properties of the entire brain.

Original languageEnglish
Pages (from-to)926-929
Number of pages4
JournalProceedings - International Symposium on Biomedical Imaging
DOIs
Publication statusPublished - 21 Jul 2015
Event12th IEEE International Symposium on Biomedical Imaging, ISBI 2015 - Brooklyn, United States
Duration: 16 Apr 201519 Apr 2015

Bibliographical note

Publisher Copyright:
© 2015 IEEE.

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