Higher order positive semidefinite diffusion tensor imaging

Liqun Qi, Gaohang Yu, Ed X. Wu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

119 Citations (Scopus)

Abstract

Due to the well-known limitations of diffusion tensor imaging, high angular resolution diffusion imaging (HARDI) is used to characterize non-Gaussian diffusion processes. One approach to analyzing HARDI data is to model the apparent diffusion coefficient (ADC) with higher order diffusion tensors. The diffusivity function is positive semidefinite. In the literature, some methods have been proposed to preserve positive semidefiniteness of second order and fourth order diffusion tensors. None of them can work for arbitrarily high order diffusion tensors. In this paper, we propose a comprehensive model to approximate the ADC profile by a positive semidefinite diffusion tensor of either second or higher order. We call this the positive semidefinite diffusion tensor (PSDT) model. PSDT is a convex optimization problem with a convex quadratic objective function constrained by the nonnegativity requirement on the smallest Z-eigenvalue of the diffusivity function. The smallest Z-eigenvalue is a computable measure of the extent of positive definiteness of the diffusivity function. We also propose some other invariants for the ADC profile analysis. Experiment results show that higher order tensors could improve the estimation of anisotropic diffusion and that the PSDT model can depict the characterization of diffusion anisotropy which is consistent with known neuroanatomy.
Original languageEnglish
Pages (from-to)416-433
Number of pages18
JournalSIAM Journal on Imaging Sciences
Volume3
Issue number3
DOIs
Publication statusPublished - 3 Dec 2010

Keywords

  • Apparent diffusion coefficient
  • Convex optimization problem
  • Invariants
  • Positive semidefinite diffusion tensor
  • Z-eigenvalue

ASJC Scopus subject areas

  • Mathematics(all)
  • Applied Mathematics

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