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Activity Number:
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322
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, July 31, 2007 : 2:00 PM to 3:50 PM
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Sponsor:
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Biometrics Section
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| Abstract - #308205 |
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Title:
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Statistical Analysis of Diffusion Tensors in Diffusion-Weighted Magnetic Resonance Image Data
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Author(s):
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Hongtu Zhu*+ and Heping Zhang and Joseph G. Ibrahim and Bradley G. Peterson
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Companies:
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The University of North Carolina at Chapel Hill and Yale University and The University of North Carolina at Chapel Hill and Columbia University
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Address:
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Department of Biostatistics, Chapel Hill, NC, 27599-3804,
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Keywords:
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Diffusion tensors ; Eigenvalue ; Eigenvector ; Pseudo-likelihood rato ; Weighted least square
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Abstract:
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Diffusion tensor imaging has been widely used to construct the structure and orientation of fibers in biological tissues. The aim of this study is to provide a comprehensive theoretical framework of statistical inference for quantifying the effects of noise on diffusion tensors, on their eigenvalues and eigenvectors, and on their morphological classification. We propose a semiparametric model parametric model to account for noise in diffusion-weighted images. We then develop a one-step, weighted least-squares estimate of the tensors and justify use of the one-step estimates based on our theoretical framework and computational results. We also quantify the effects of noise on the eigenvalues and eigenvectors of the estimated tensors by establishing their limiting distributions. We construct pseudo-likelihood ratio statistics to classify tensor morphologies.
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