A new framework for analyzing structural volume changes of longitudinal brain MRI data

Authors:
Bérengère Aubert-Broche;Vladimir S. Fonov;Daniel García-Lorenzo;Abderazzak Mouiha;Nicolas Guizard;Pierrick Coupé;Simon F. Eskildsen;D. Louis Collins
Affiliations:
Montreal Neurological Institute, McGill University, Canada;Montreal Neurological Institute, McGill University, Canada;Montreal Neurological Institute, McGill University, Canada,ICM, UPMC/INSERM UMR975 CNRS UMR7225, Hôpital de la Salpêtrière, Paris, France;Montreal Neurological Institute, McGill University, Canada;Montreal Neurological Institute, McGill University, Canada;Montreal Neurological Institute, McGill University, Canada,Laboratoire Bordelais de Recherche en Informatique, Unité Mixte de Recherche CNRS (UMR 5800), Bordeaux, France;Montreal Neurological Institute, McGill University, Canada,Aarhus University, Denmark;Montreal Neurological Institute, McGill University, Canada
Venue:
STIA'12 Proceedings of the Second international conference on Spatio-temporal Image Analysis for Longitudinal and Time-Series Image Data
Year:
2012

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Abstract

Cross-sectional analysis of longitudinal MRI data might be sub-optimal as each dataset is analyzed independently. In this study, we evaluate how much variability can be reduced by analyzing structural volume changes of 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 non-linear templates that are then registered to a population template. The longitudinal classification is composed of a 4D EM algorithm, using a priori classes computed by averaging the tissue classes of all time points obtained cross-sectionally. To study the impact of these two steps, we apply the framework completely (called LL method: Longitudinal registration and Longitudinal classification) and partially (LC method: Longitudinal registration and Cross-sectional classification) and compare these to a standard cross-sectional framework (CC method: Cross-sectional registration and Cross-sectional classification). The three methods are applied to (1) a scan-rescan database to analyze the reliability and to (2) the NIH pediatric population to compare the GM and WM growth trajectories, evaluated with a linear mixed-model. The LL method, and the LC method to a lesser extent, significantly reduce the variability in the measurements in the scan-rescan study and give the best fitted GM and WM growth models with the NIH pediatric database. The results confirm that both steps of the longitudinal framework reduce the variability and improve the accuracy compared to the cross-sectional framework, with longitudinal classification yielding the greatest impact.