Indirect pet-pet image registration to monitor lung cancer tumor

This paper deals with registering 3D PET images in order to monitor lung tumor evolution. Registering directly two PET images, taken at different stages of a cancer therapy, leads to deforming the tumor of the moving image to take the shape of the fixed image, loosing the tumor evolution information. This results in aberrant medical diagnosis. In order to solve this problem, we propose an indirect registration method that consists of processing pairs of CT-PET images. The CT images acquired at each stage are first registered to estimate anatomical transformations. The free-form deformation obtained is then applied to the corresponding PET images. The reconstructed PET images can be compared and used to monitor the tumor. The volume ratio and radiation density are calculated to assess the evolution of the tumor and evaluate the effectiveness of a therapy. The proposed iconic registration method is based on a B-Spline deformable model and mutual information. Two approaches have been used to validate the proposed method. First, we used phantoms to simulate the evolution of a tumor. The second approach consisted of simulating a tumor within real images. Quantitative measures show that our registration method keeps invariant volume and density distribution ratios of the tumor within PET images. This leads to improved tumor localisation and better evaluation of the efficiency of therapies.