Contrast-enhanced dual-energy CT synthesis from single energy CT using diffusion model

Dual-Energy CT (DECT) has risen to prominence as a valuable instrument in diagnostic imaging, boasting a range of clinical applications. Contrast-DECT (C-DECT) is particularly useful in clinical by generating iodine density map, which could benefit radiation oncologists in treatment planning process. However, DECT scanners are not widely equipped among the radiation therapy centers. Moreover, side effects from iodine agents restrict the use of DECT iodine contrast imaging for all patients. The purpose of this work is to generate synthetic C-DECT images based on non-contrast single-energy CT (SECT) via deep learning (DL) method. 108 head-and-neck cancer patients' images were retrospectively investigated in this work. All patients were scanned with non-contrast SECT and contrast DECT protocols. A conditional Denoising Diffusion Probalistic Model (DDPM) was implemented to generate synthetic High-energy CT (H-CT) and Low-energy CT (L-CT). The training and application dataset was separated strictly, 100 patients' data were used as the training dataset and the rest eight patients' data were used as the application dataset. The performance of the proposed method was evaluated with three quantitative metrics including Mean Absolute Error (MAE), Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR). For H-CT and L-CT, the quantitative evaluation results of MAE, SSIM and PSNR are 19.15±2.23 (HU) and 23.34±3.45 (HU), 0.74±0.13 and 0.75±0.19, 28.13±2.83 (dB) and 28.18±3.55 (dB), respectively. This approach holds potential significance for radiation therapy facilities lacking DECT scanners, as well as for specific patients who may not be suitable candidates for iodine agent injection.