Positive definiteness of Diffusion Kurtosis Imaging

Shenglong Hu, Zheng Hai Huang, Hong Yan Ni, Liqun Qi

Research output: Journal article publicationJournal articleAcademic researchpeer-review

23 Citations (Scopus)

Abstract

Difiusion Kurtosis Imaging (DKI) is a new Magnetic Resonance Imaging (MRI) model to characterize the non-Gaussian difiusion behavior in tissues. In reality, the term, in the extended Stejskal and Tanner equation of DKI should be positive for an appropriate range of b-values to make sense physically. The positive definiteness of the above term reects the signal attenuation in tissues during imaging. Hence, it is essential for the validation of DKI. In this paper, we analyze the positive definiteness of DKI. We first characterize the positive definiteness of DKI through the positive definiteness of a tensor constructed by difiusion tensor and difiusion kurtosis tensor. Then, a conic linear optimization method and its simplified version are proposed to handle the positive definiteness of DKI from the perspective of numerical computation. Some preliminary numerical tests on both synthetical and real data show that the method discussed in this paper is promising.
Original languageEnglish
Pages (from-to)57-75
Number of pages19
JournalInverse Problems and Imaging
Volume6
Issue number1
DOIs
Publication statusPublished - 1 Feb 2012

Keywords

  • Conic linear programming
  • Diffusion kurtosis tensor
  • Positive definiteness

ASJC Scopus subject areas

  • Analysis
  • Control and Optimization
  • Discrete Mathematics and Combinatorics
  • Modelling and Simulation

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