Conditional generative adversarial networks to generate pseudo low monoenergetic CT image from a single-tube voltage CT scanner

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.

Automated summary

This study successfully generated pseudo low monoenergetic CT images of the abdomen using cGAN technology, which had good quality similar to monochromatic images obtained with DECT software.