This study describes a method to perform an image to image translation by means of a deep generative model. Its purpose is to obtain, from abdomen CT images with no contrast, contrast enhanced CTs and vice versa. The entire study was carried out through a six-month internship in Paris at the Ecole Nationale Supérieure des Télécommunications (TELECOM Paris), in the department of Image, Data, Signal (IDS). The medical images are obtained from different databases that are available online, and they specifically analyse only the abdominal component of patients. Starting from the lungs to the femoral head. In particular, the generative model is composed of Cycle Consistent Adversarial Networks (called CycleGAN), whose main properties are based on the recent model described by Zhu et al.