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

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journal contribution
posted on 2015-09-18, 13:44 authored by Diwei ZhouDiwei Zhou, Ian L. Dryden, Alexey Koloydenko, K.M.R. Audenaert, Li Bai
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 visualization. Some properties of the measures are derived and practical considerations are discussed, with some examples. Second we discuss weighted Procrustes methods for DTI 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.


The second author acknowledges support from a Royal Society Wolfson Research Merit Award and EPSRC grant EP/K022547/1.



  • Science


  • Mathematical Sciences

Published in

Journal of Applied Statistics


ZHOU, D. ...et al., 2016. Regularisation, interpolation and visualisation of diffusion tensor images using non-Euclidean statistics. Journal of Applied Statistics 43(5), pp.943-978.


© Taylor & Francis


  • AM (Accepted Manuscript)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Publication date



This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of Applied Statistics on 23rd September 2015, available online: http://dx.doi.org/10.1080/02664763.2015.1080671


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