An efficient incremental strategy for constrained groupwise registration based on symmetric pairwise registration

Abstract

Neuroimaging studies carried out on healthy or pathological cohorts generally require to map the set of all subject images in a common coordinate system thanks to some registration techniques. This is usually done by considering an arbitrarily chosen reference image (also called template) on which all other images are registered. However, the choice of the template can significantly impact the results and the interpretation of statistical comparisons between cohorts for both functional and morphometric studies. This is why we propose an efficient strategy for the automatic building of study-specific templates. The main contribution of this work is to propose a method, based on a symmetric formulation of the pairwise registration problem, that enables to enforce the template image to be at the geometric center of the population with little computational overhead. Moreover, the template image is estimated in an incremental way, thus being conveniently updatable when considering additional images. This property is of major interest for current clinical studies which involve very large databases that are constantly growing. Experiments on both synthetic and real data highlight the good convergence properties of the approach compared to a standard strategy based on pairwise registration. The benefit of using the proposed symmetric formulation in the standard template construction strategy is also pointed out.