Average brain models: a convergence study
Computer Vision and Image Understanding - Special issue on analysis of volumetric image
Transport of Relational Structures in Groups of Diffeomorphisms
Journal of Mathematical Imaging and 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
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
Schild's Ladder for the parallel transport of deformations in time series of images
IPMI'11 Proceedings of the 22nd international conference on Information processing in medical imaging
A log-euclidean framework for statistics on diffeomorphisms
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Regional flux analysis of longitudinal atrophy in alzheimer's disease
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Mixed-Effects shape models for estimating longitudinal changes in anatomy
STIA'12 Proceedings of the Second international conference on Spatio-temporal Image Analysis for Longitudinal and Time-Series Image Data
A hierarchical geodesic model for diffeomorphic longitudinal shape analysis
IPMI'13 Proceedings of the 23rd international conference on Information Processing in Medical Imaging
Geodesics, Parallel Transport & One-Parameter Subgroups for Diffeomorphic Image Registration
International Journal of Computer Vision
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Mapping the effects of different clinical conditions on the evolution of the brain structural changes is of central interest in the field of neuroimaging. A reliable description of the cross-sectional longitudinal changes requires the consistent integration of intra and inter-subject variability in order to detect the subtle modifications in populations. In computational anatomy, the changes in the brain are often measured by deformation fields obtained through non rigid registration, and the stationary velocity field (SVF) parametrization provides a computationally efficient registration scheme. The aim of this study is to extend this framework into an efficient and robust multilevel one for accurately modeling the longitudinal changes in populations. This setting is used to investigate the subtle effects of the positivity of the CSF Aβ1-42 levels on brain atrophy in healthy aging. Thanks to the higher sensitivity of our framework, we obtain statistically significant results that highlight the relationship between brain damage and positivity to the marker of Alzheimer's disease and suggest the presence of a presymptomatic pattern of the disease progression.