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.
This paper deals with enhancing the formation of PET images. Physiological motion, such as breathing, may cause significant alteration of image quality. Correction methods include gated acquisitions that significantly increase the acquisition time. In this paper we propose an original method for reducing respiratory motion artefacts in PET images. It is based on synchronous acquisition of PET and CT data with a spirometer. CT images are acquired at each step of a subdivided respiratory cycle, and registered to estimate the body transformations. Then PET data is indirectly registered and corrected for attenuation before reconstructing a PET image with enhanced quality. This method has been validated using a specific phantom experimentation. Results show that the method brings improved accuracy in tumour volume representation. In addition, the PET imaging clinical protocol is unchanged: our method does not increase the acquisition time nor constrain the patient breathing.