Tensor-Based Morphometry with Mappings Parameterized by Stationary Velocity Fields in Alzheimer's Disease Neuroimaging Initiative

Authors:
Matías Nicolás Bossa;Ernesto Zacur;Salvador Olmos
Affiliations:
GTC, Aragon Institute of Engineering Research (I3A), University of Zaragoza, Spain;GTC, Aragon Institute of Engineering Research (I3A), University of Zaragoza, Spain;GTC, Aragon Institute of Engineering Research (I3A), University of Zaragoza, Spain
Venue:
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
Year:
2009

Citing 4
Cited 0

Computing Large Deformation Metric Mappings via Geodesic Flows of Diffeomorphisms

International Journal of Computer Vision
Symmetric Log-Domain Diffeomorphic Registration: A Demons-Based Approach

MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
Registration of Anatomical Images Using Paths of Diffeomorphisms Parameterized with Stationary Vector Field Flows

International Journal of Computer Vision
Deformable templates using large deformation kinematics

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Tensor-based morphometry (TBM) is an analysis technique where anatomical information is characterized by means of the spatial transformations between a customized template and observed images. Therefore, accurate inter-subject non-rigid registration is an essential prerrequisite. Further statistical analysis of the spatial transformations is used to highlight some useful information, such as local statistical differences among populations. With the new advent of recent and powerful non-rigid registration algorithms based on the large deformation paradigm, TBM is being increasingly used. In this work we evaluate the statistical power of TBM using stationary velocity field diffeomorphic registration in a large population of subjects from Alzheimer's Disease Neuroimaging Initiative project. The proposed methodology provided atrophy maps with very detailed anatomical resolution and with a high significance compared with results published recently on the same data set.