Multimodal Image Registration using Statistically Constrained Deformable Multimodels

The registration of multimodal images remains an intricate issue, especially when the multimodal image pair shows non overlapping structures, missing data, noise or outliers. In this paper, we present a deformable model-based technique for the rigid registration of 2 0 and 3D multimodal images. The deformable model embeds a priori knowledge of the spatial correspondence and statistical variability of the different (eventually non overlapping) image features which are used in the registration procedure. The method is applied to the intrasubject registration of medical MR/SPECT images of the brain b y constructing a deformable model incorporating information on both MR (head) and SPECT (brain) contours. In an off-line training procedure, the spatial relations between head and brain as well as the anatomical variations of these two structures are learned. The registration is performed by minimizing an objective function involving the contours of the MR and SPECT images.