Combining a Breathing Model and Tumor-Specific Rigidity Constraints for Registration of CT-PET Thoracic Data

Diagnosis and therapy planning in oncology applications often rely on the joint ex-ploitation of two complementary imaging modalities, namely Computerized Tomography (CT) and Positron Emission Tomography (PET). While recent technical advancements in combined CT/PET scanners provide 3D CT and PET data of the thoracic region with the patient in the same global position, current image data registration methods do not account for breathing-induced anatomical changes in the thoracic region, which remains an important limitation. This paper deals with the problem of 3D registration of CT thoracic image volumes acquired at two different instants of the breathing cycle and PET volumes of thoracic regions. In order to guarantee physiologically-plausible deformations, we present a novel method to incorporate a breathing model in a non-linear registration procedure. The approach is based on simulating intermediate lung shapes between the two 3D lung surfaces segmented on the CT volumes and finding the one most resembling lung surface segmented on the PET data. To compare lung surfaces, a shape registration method is used, aligning anatomical landmark points automatically selected based on lo