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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/17103

Title: Procrustes analysis of diffusion tensor data
Authors: Zhou, Diwei
Dryden, Ian L.
Koloydenko, Alexey
Bai, Li
Issue Date: 2009
Publisher: Curran Associates, Inc.
Citation: ZHOU, D. ... et al., 2009. Procrustes analysis of diffusion tensor data. IN: Proceedings of the 17th Annual Conference of International Society for Magnetic Resonance in Medicine, USA, p.3583.
Abstract: Diffusion tensor imaging (DTI) is becoming increasingly important in clinical studies of diseases such as multiple sclerosis and schizophrenia, and also in investigating brain connectivity. Hence, there is a growing need to process diffusion tensor (DT) images within a statistical framework based on appropriate mathematical metrics. However, the usual Euclidean operations are often unsatisfactory for diffusion tensors due to the symmetric, positive-definiteness property. A DT is a type of covariance matrix and non-Euclidean metrics have been adapted naturally for DTI processing [1]. In this paper, Procrustes analysis has been used to define a weighted mean of diffusion tensors that provides a suitable average of a sample of tensors. For comparison, six geodesic paths between a pair of diffusion tensors are plotted using the Euclidean as well as various non-Euclidean distances. We also propose a new measure of anisotropy -Procrustes anisotropy (PA). Fractional anisotropy (FA) and PA maps from an interpolated and smoothed diffusion tensor field from a healthy human brain are shown as an application of the Procrustes method.
Description: This is a conference paper.
Sponsor: European Commission FP6 Marie Curie programme through the CMIAG Research Training Network
Version: Accepted for publication
URI: https://dspace.lboro.ac.uk/2134/17103
Appears in Collections:Conference Papers and Presentations (Maths)

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