Multivariate analysis of eigenvalues and eigenvectors in tensor based morphometry

Vidya Rajagopalan; Armin Schwartzman; Xue Hua; Alex Leow; Paul Thompson; Natasha Lepore

doi:10.1117/12.2073737

28 January 2015 Multivariate analysis of eigenvalues and eigenvectors in tensor based morphometry

Vidya Rajagopalan, Armin Schwartzman, Xue Hua, Alex Leow, Paul Thompson, Natasha Lepore

Proceedings Volume 9287, 10th International Symposium on Medical Information Processing and Analysis; 928707 (2015) https://doi.org/10.1117/12.2073737
Event: Tenth International Symposium on Medical Information Processing and Analysis, 2014, Cartagena de Indias, Colombia

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.

Citation Download Citation

Vidya Rajagopalan, Armin Schwartzman, Xue Hua, Alex Leow, Paul Thompson, and Natasha Lepore "Multivariate analysis of eigenvalues and eigenvectors in tensor based morphometry", Proc. SPIE 9287, 10th International Symposium on Medical Information Processing and Analysis, 928707 (28 January 2015); https://doi.org/10.1117/12.2073737

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