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Proc IEEE Int Symp Biomed Imaging. 2008 May;2008:1115-1118. doi: 10.1109/ISBI.2008.4541196. Epub 2008 Jun 13.

A NEW REGISTRATION METHOD BASED ON LOG-EUCLIDEAN TENSOR METRICS AND ITS APPLICATION TO GENETIC STUDIES.

Author information

1
Laboratory of Neuro Imaging, Department of Neurology, UCLA Los Angeles, CA 90095, USA.
2
Asclepios Research Project, INRIA, 06902 Sophia-Antipolis Cedex, France.
3
Centre for Magnetic Resonance, University of Queensland.
4
Genetic Epidemiology Lab, Queensland Institute of Medical Research, Queensland 4029, Australia.

Abstract

In structural brain MRI, group differences or changes in brain structures can be detected using Tensor-Based Morphometry (TBM). This method consists of two steps: (1) a non-linear registration step, that aligns all of the images to a common template, and (2) a subsequent statistical analysis. The numerous registration methods that have recently been developed differ in their detection sensitivity when used for TBM, and detection power is paramount in epidemological studies or drug trials. We therefore developed a new fluid registration method that computes the mappings and performs statistics on them in a consistent way, providing a bridge between TBM registration and statistics. We used the Log-Euclidean framework to define a new regularizer that is a fluid extension of the Riemannian elasticity, which assures diffeomorphic transformations. This regularizer constrains the symmetrized Jacobian matrix, also called the deformation (or strain) tensor. We applied our method to an MRI dataset from 40 fraternal and identical twins, to revealed voxelwise measures of average volumetric differences in brain structure for subjects with different degrees of genetic resemblance.

KEYWORDS:

Brain Imaging; Genetics; MRI; Registration; Statistical analysis

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