Positron emission tomography (PET) is a powerful tool because it can acquire quantitative functional images. To obtain quantitative images, attenuation correction (AC) is indispensable, but it sometimes fails in cases such as CT system problems or other causes. PET images not having CT images also appear in measurements using small animal PET systems that are not combined with a CT system or a PET/MRI system. In this case, the generation of CT images from PET images using deep learning (DL) may be a possible solution. Consequently we tried this approach using measured small animal PET/CT images. We used pix2pix generative adversarial networks (GANs) for deep learning. After training the neural network using some of the measured small animal PET/CT image pairs, we predicted synthetic CT (sCT) images from the PET images of rat heads and compared them with the measured CT images. After the training, we could generate sCT images that had similar structures to the rat's skull, although there were some differences observed in the headrest parts. We conclude that sCT image generation from PET images is possible and has the potential to be used for AC in small animal PET systems.