Motion compensated image reconstruction of respiratory gated PET/CT

Detectability of lung tumors in PET images is severely compromised owing to respiratory motion. A model based motion compensation method for image reconstruction from respiratory-gated PET/CT data is presented. The object at each gate is modeled as a deformation of the object at a reference gate. Gated CT images are co-registered to the image at the reference gate to estimate object motion. This motion information is in turn used as part of the system model in the forward and back projection steps of the reconstruction algorithm. We simulated the respiratory-gated PET/CT acquisition of the mathematical NCAT phantom with realistic respiratory motion. The model-based approach was compared with post-reconstruction registration of independently reconstructed gates. Simulation results show that the model-based approach achieves better tumor detectability than methods based on post-reconstruction registration