Objective: The objectives of this study are: (1) to develop a convolutional neural network model that yields pseudo high-energy CT (CT pseudo_high ) from simple image processed low-energy CT (CT low ) images, and (2) to create a pseudo iodine map (IM pseudo ) and pseudo virtual non-contrast (VNC pseudo ) images for thoracic and abdominal regions. Methods: Eighty patients who underwent dual-energy CT (DECT) examinations were enrolled. The data obtained from 55, 5, and 20 patients were used for training, validation, and testing, respectively. The ResUnet model was used for image generation model and was trained using CT low and high-energy CT (CT high ) images. The proposed model performance was evaluated by calculating the CT values, image noise, mean absolute errors (MAEs), and histogram intersections (HIs). Results: The mean difference in the CT values between CT pseudo_high and CT high images were less than 6 Hounsfield unit (HU) for all evaluating patients. The image noise of CT pseudo_high was significantly lower than that of CT high . The mean MAEs was less than 15 HU, and HIs were almost 1.000 for all the patients. The evaluation metrics of IM and VNC exhibited the same tendency as that of the comparison between CT pseudo_high and CT high images. Conclusions: Our results indicated that the proposed model enables to obtain the DECT images and material-specific images from only single-energy CT images. Advances in knowledges: We constructed the CNN-based model which can generate pseudo DECT image and DECT-derived material-specific image using only simple image-processed CT low images for the thoracic and abdominal regions.