Open Access
March 2013 Local tests for identifying anisotropic diffusion areas in human brain with DTI
Tao Yu, Chunming Zhang, Andrew L. Alexander, Richard J. Davidson
Ann. Appl. Stat. 7(1): 201-225 (March 2013). DOI: 10.1214/12-AOAS573

Abstract

Diffusion tensor imaging (DTI) plays a key role in analyzing the physical structures of biological tissues, particularly in reconstructing fiber tracts of the human brain in vivo. On the one hand, eigenvalues of diffusion tensors (DTs) estimated from diffusion weighted imaging (DWI) data usually contain systematic bias, which subsequently biases the diffusivity measurements popularly adopted in fiber tracking algorithms. On the other hand, correctly accounting for the spatial information is important in the construction of these diffusivity measurements since the fiber tracts are typically spatially structured. This paper aims to establish test-based approaches to identify anisotropic water diffusion areas in the human brain. These areas in turn indicate the areas passed by fiber tracts. Our proposed test statistic not only takes into account the bias components in eigenvalue estimates, but also incorporates the spatial information of neighboring voxels. Under mild regularity conditions, we demonstrate that the proposed test statistic asymptotically follows a $\chi^{2}$ distribution under the null hypothesis. Simulation and real DTI data examples are provided to illustrate the efficacy of our proposed methods.

Citation

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Tao Yu. Chunming Zhang. Andrew L. Alexander. Richard J. Davidson. "Local tests for identifying anisotropic diffusion areas in human brain with DTI." Ann. Appl. Stat. 7 (1) 201 - 225, March 2013. https://doi.org/10.1214/12-AOAS573

Information

Published: March 2013
First available in Project Euclid: 9 April 2013

zbMATH: 06171269
MathSciNet: MR3086416
Digital Object Identifier: 10.1214/12-AOAS573

Keywords: Brain tissue , diffusion tensor , eigenvalue , fiber tracts , local test , quantitative scalar

Rights: Copyright © 2013 Institute of Mathematical Statistics

Vol.7 • No. 1 • March 2013
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