High-dimensional multi-modal image registration

Abstract

This paper presents a Bayesian framework for generating inter-subject high-dimensional transformations between two multi-modal image sets of the brain. In this framework, the estimated transformations are generated by using the maximal information about the underlying neuroanatomy present in each of the different modalities. This modality independent registration framework is achieved by using the Bayesian paradigm and jointly estimating the posterior densities associated with the multi-modal image sets and the high dimensional registration transformation mapping the two subjects. The methods presented do not assume that the same modalities were used to image the two subjects. To maximally use the information present in all the modalities, relative entropy (or Kullback Leibler divergence) between the estimated posteriors is minimized to estimate the registration. The high-dimensional registration is constrained to be diffeomorphic by using the large deformation fluid formulation. We also show that the general framework optimally unifies the processes of segmentation and registration of human brain anatomy. Given a set of multi-modal intensity images of an individual subject and a geometric atlas prior, we jointly estimate the posterior that represents the structure of that subject's neuroanatomy and the transformation that maps the space of the atlas prior to the space of the estimated posterior, capturing high-dimensional local variability. © Springer-Verlag Berlin Heidelberg 2003.