Regularisation, interpolation and visualisation of diffusion tensor images using non-Euclidean statistics

Diwei Zhou, Ian Dryden, Alexey A. Koloydenko, Koenraad Audenaert, Li Bai

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Abstract

Practical statistical analysis of diffusion tensor images is considered, and we focus primarily on methods that use metrics based on Euclidean distances between powers of diffusion tensors. First we describe a family of
anisotropy measures based on a scale invariant power-Euclidean metric, which are useful for visualisation. Some properties of the measures are derived and practical considerations are discussed, with some examples. Second we discuss weighted Procrustes methods for diffusion tensor interpolation and smoothing, and we compare methods based on different metrics on a set of examples as well as analytically. We establish a key relationship between the
principal-square-root-Euclidean metric and the size-and-shape Procrustes metric on the space of symmetric positive semi-definite tensors. We explain, both analytically and by experiments, why the size-and-shape Procrustes metric may be preferred in practical tasks of interpolation, extrapolation, and smoothing, especially when observed tensors are degenerate or when a moderate degree of tensor swelling is desirable. Third we introduce regularisation methodology, which is demonstrated to be useful for highlighting features of prior interest and potentially for segmentation. Finally, we compare several metrics in a dataset of human brain diffusion-weighted MRI, and point out similarities between several of the non-Euclidean metrics but important differences with the commonly used Euclidean metric.
Original languageEnglish
Pages (from-to)943-978
Number of pages36
JournalJournal of Applied Statistics
Volume43
Issue number5
Early online date23 Sep 2015
DOIs
Publication statusPublished - 2016

Keywords

  • anisotropy
  • metric
  • positive definite
  • power
  • Procrustes
  • Riemannian
  • smoothing
  • weighted Fréchet mean

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