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Proceedings Paper

Multivariate analysis of eigenvalues and eigenvectors in tensor based morphometry
Author(s): Vidya Rajagopalan; Armin Schwartzman; Xue Hua; Alex Leow; Paul Thompson; Natasha Lepore
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Paper Abstract

We develop a new algorithm to compute voxel-wise shape differences in tensor-based morphometry (TBM). As in standard TBM, we non-linearly register brain T1-weighed MRI data from a patient and control group to a template, and compute the Jacobian of the deformation fields. In standard TBM, the determinants of the Jacobian matrix at each voxel are statistically compared between the two groups. More recently, a multivariate extension of the statistical analysis involving the deformation tensors derived from the Jacobian matrices has been shown to improve statistical detection power.7 However, multivariate methods comprising large numbers of variables are computationally intensive and may be subject to noise. In addition, the anatomical interpretation of results is sometimes difficult. Here instead, we analyze the eigenvalues and the eigenvectors of the Jacobian matrices. Our method is validated on brain MRI data from Alzheimer’s patients and healthy elderly controls from the Alzheimer’s Disease Neuro Imaging Database.

Paper Details

Date Published: 28 January 2015
PDF: 5 pages
Proc. SPIE 9287, 10th International Symposium on Medical Information Processing and Analysis, 928707 (28 January 2015); doi: 10.1117/12.2073737
Show Author Affiliations
Vidya Rajagopalan, Children's Hospital Los Angeles (United States)
Armin Schwartzman, North Carolina State Univ. (United States)
Xue Hua, The Univ. of Southern California (United States)
Alex Leow, Univ. of Illinois at Chicago (United States)
Paul Thompson, The Univ. of Southern California (United States)
Natasha Lepore, Children's Hospital Los Angeles (United States)

Published in SPIE Proceedings Vol. 9287:
10th International Symposium on Medical Information Processing and Analysis
Eduardo Romero; Natasha Lepore, Editor(s)

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