Automatic non-rigid registration of MR and PET images

Registration of functional PET and MR images is a necessary step for combining functional information from PET images with anatomical information in MR images. But, the methods published are non-automatic or rigid body transformation. In this paper, we present a method that mapping PET image onto the MR image with automatic non-rigid transformation. The method is largely composed of two parts. First part is the segmentation and extracting the features of both NM and PET by using FCM and morphological methods. And, second part is the non-rigid mapping of PET image onto the PET template and MR image onto NIR template. The templates are made from 20 PET images and 20 NIR images each other. And, the NIR template is registered with PET template. In non-rigid mapping, we use Bayesian framework in which statistical information on the imaging process is combined with prior information on expected template deformations to make inferences about the parameters of the deformation field. The method newly defines intensity similarity between the deforming scan and the target brain. Intensity similarity combined with prior information is used to generate deformation field. We applied our algorithm to PET and Tl-weighted MR images from many patients. Physicians validated the registered images. And we got the satisfactory results.