Pseudo dual-energy CT-derived iodine mapping using single-energy CT data based on a convolution neural network

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.

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).

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.

Our results indicated that the proposed model enables to obtain the DECT images and material-specific images from only single-energy CT images.

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.