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Analysis of diffusion tensor magnetic resonance imaging data using principal component analysis

Papadakis, N. G., Zheng, Y. and Wilkinson, I. D. (2003) Analysis of diffusion tensor magnetic resonance imaging data using principal component analysis. Physics in Medicine and Biology, 48 (24). N343-N350. ISSN 1361-6560

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To link to this item DOI: 10.1088/0031-9155/48/24/N01

Abstract/Summary

An analysis method for diffusion tensor (DT) magnetic resonance imaging data is described, which, contrary to the standard method (multivariate fitting), does not require a specific functional model for diffusion-weighted (DW) signals. The method uses principal component analysis (PCA) under the assumption of a single fibre per pixel. PCA and the standard method were compared using simulations and human brain data. The two methods were equivalent in determining fibre orientation. PCA-derived fractional anisotropy and DT relative anisotropy had similar signal-to-noise ratio (SNR) and dependence on fibre shape. PCA-derived mean diffusivity had similar SNR to the respective DT scalar, and it depended on fibre anisotropy. Appropriate scaling of the PCA measures resulted in very good agreement between PCA and DT maps. In conclusion, the assumption of a specific functional model for DW signals is not necessary for characterization of anisotropic diffusion in a single fibre.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Life Sciences > School of Biological Sciences > Department of Bio-Engineering
ID Code:33487
Publisher:IOP Publishing

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