The primary aim of this research is to implement a Generative Adversarial Network (GAN) to synthesize CT images from non-attenuation corrected (NAC) FDG-PET images. Registration of multi-modality images (NAC-PET to CT) is a challenging problem due to variability of tissue or organ appearance. Hence, in order to reduce the variability, this work will investigate the use of GAN generated synthetic CT images to perform PET/CT registration.
Synthetic generation of CT image from non-attenuation corrected FDG-PET image using GANs and its application to whole-body PET/CT registration.
Type: MA thesis
Status: finished
Date: June 7, 2020 - December 7, 2020
Supervisors: Maximilian Reymann, Vijay Shah (Siemens Healthineers), Philipp Ritt ( Nuklearmedizinische Klinik, Universitätsklinikum Erlangen)