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|Title: ||Procrustes analysis of diffusion tensor data|
|Authors: ||Zhou, Diwei|
Dryden, Ian L.
|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 . 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|
|Appears in Collections:||Conference Papers (Maths)|
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