Paper Info
Title
A new method for structural volume analysis of longitudinal brain MRI data and its application in studying the growth trajectories of anatomical brain structures in childhood.
Abstract
Cross-sectional analysis of longitudinal anatomical magnetic resonance imaging (MRI) data may be suboptimal as each dataset is analyzed independently. In this study, we evaluate how much variability can be reduced by analyzing structural volume changes in longitudinal data using longitudinal analysis. We propose a two-part pipeline that consists of longitudinal registration and longitudinal classification. The longitudinal registration step includes the creation of subject-specific linear and nonlinear templates that are then registered to a population template. The longitudinal classification step comprises a four-dimensional expectation-maximization algorithm, using a priori classes computed by averaging the tissue classes of all time points obtained cross-sectionally.
Year
DOI
Venue
2013
10.1016/j.neuroimage.2013.05.065
NeuroImage
Keywords
Field
DocType
Longitudinal brain MRI,Longitudinal analysis,Structural volume analysis,Brain development,Growth trajectories
Brain development,Data mining,Developmental psychology,Population,Growth model,Brain mri,A priori and a posteriori,Psychology,Mixed model,Magnetic resonance imaging
Journal
Volume
ISSN
Citations 
82
1053-8119
13
PageRank 
References 
Authors
0.70
16
8
Name
Order
Citations
PageRank
Berengere Aubert-Broche116614.12
Vladimir Fonov247820.32
Daniel García-Lorenzo31598.07
Abderazzak Mouiha4161.48
nicolas guizard51025.89
Pierrick Coupé6120960.13
simon fristed eskildsen752033.87
D. Louis Collins83915403.90