Conditional generative adversarial networks to generate pseudo low monoenergetic CT image from a single-tube voltage CT scanner. (March 2021) - Funama, Yoshinori; Oda, Seitaro; Kidoh, Masafumi; Nagayama, Yasunori; Goto, Makoto; Sakabe, Daisuke; Nakaura, Takeshi
Highlights: Pseudo 55-keV images of the abdomen was generated from 120-kVp images using cGAN. The PSNR and SSIM had almost constant values from epochs 50 to 500. Pseudo 55-keV images had sufficient image quality compared with actual 55-keV VMIs. Using the final model, similar VMI images can be obtained without a dual-energy scan. Abstract: Purpose: To generate pseudo low monoenergetic CT images of the abdomen from 120-kVp CT images with cGAN. Materials and Methods: We retrospectively included 48 patients who underwent contrast-enhanced abdominal CT using dual-energy CT. We reconstructed paired data sets of 120 kVp CT images and virtual low monoenergetic (55-keV) CT images. cGAN was prepared to generate pseudo 55-keV CT images from 120-kVp CT images. The pseudo 55 keV CT images in epoch 10, 50, 100, and 500 were compared to the 55 keV images generated using peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). Results: The PSNRs were 28.0, 28.5, 28.6, and 28.8 at epochs 10, 50, 100, and 500, respectively. The SSIM was approximately constant from epochs 50 to 500. Conclusion: Pseudo low monoenergetic abdominal CT images were generated from 120-kVp CT images using cGAN, and the images had good quality similar to that of monochromatic images obtained with DECT software.