5 research outputs found
A Comprehensive MRI Study of Over 2000 Subjects
The incomplete-hippocampal-inversion (IHI), also known as malrotation, is an
atypical anatomical pattern of the hippocampus, which has been reported in
healthy subjects in different studies. However, extensive characterization of
IHI in a large sample has not yet been performed. Furthermore, it is unclear
whether IHI are restricted to the medial-temporal lobe or are associated with
more extensive anatomical changes. Here, we studied the characteristics of IHI
in a community-based sample of 2008 subjects of the IMAGEN database and their
association with extra-hippocampal anatomical variations. The presence of IHI
was assessed on T1-weighted anatomical magnetic resonance imaging (MRI) using
visual criteria. We assessed the association of IHI with other anatomical
changes throughout the brain using automatic morphometry of cortical sulci. We
found that IHI were much more frequent in the left hippocampus (left: 17%,
right: 6%, χ2−test, p < 10−28). Compared to subjects without IHI, subjects
with IHI displayed morphological changes in several sulci located mainly in
the limbic lobe. Our results demonstrate that IHI are a common left-sided
phenomenon in normal subjects and that they are associated with morphological
changes outside the medial temporal lobe
Statistical Shape Analysis of Large Datasets Based on Diffeomorphic Iterative Centroids
In this paper, we propose an approach for template-based shape analysis of large datasets, using diffeomorphic centroids as atlas shapes. Diffeomorphic centroid methods fit in the Large Deformation Diffeomorphic Metric Mapping (LDDMM) framework and use kernel metrics on currents to quantify surface dissimilarities. The statistical analysis is based on a Kernel Principal Component Analysis (Kernel PCA) performed on the set of initial momentum vectors which parametrize the deformations. We tested the approach on different datasets of hippocampal shapes extracted from brain magnetic resonance imaging (MRI), compared three different centroid methods and a variational template estimation. The largest dataset is composed of 1,000 surfaces, and we are able to analyse this dataset in 26 h using a diffeomorphic centroid. Our experiments demonstrate that computing diffeomorphic centroids in place of standard variational templates leads to similar shape analysis results and saves around 70% of computation time. Furthermore, the approach is able to adequately capture the variability of hippocampal shapes with a reasonable number of dimensions, and to predict anatomical features of the hippocampus, only present in 17% of the population, in healthy subjects
Statistical Shape Analysis of Large Datasets Based on Diffeomorphic Iterative Centroids
International audienceIn this paper, we propose an approach for template-based shape analysis of large datasets, using diffeomorphic centroids as atlas shapes. Diffeomorphic centroid methods fit in the Large Deformation Diffeomorphic Metric Mapping (LDDMM) framework and use kernel metrics on currents to quantify surface dissimilarities. The statistical analysis is based on a Kernel Principal Component Analysis (Kernel PCA) performed on the set of initial momentum vectors which parametrize the deformations. We tested the approach on different datasets of hippocampal shapes extracted from brain magnetic resonance imaging (MRI), compared three different centroid methods and a variational template estimation. The largest dataset is composed of 1,000 surfaces, and we are able to analyse this dataset in 26 h using a diffeomorphic centroid. Our experiments demonstrate that computing diffeomorphic centroids in place of standard variational templates leads to similar shape analysis results and saves around 70% of computation time. Furthermore, the approach is able to adequately capture the variability of hippocampal shapes with a reasonable number of dimensions, and to predict anatomical features of the hippocampus, only present in 17% of the population, in healthy subjects
Large deformation diffeomorphic metric curve mapping
10.1007/s11263-008-0141-9International Journal of Computer Vision803317-336IJCV