Conditional generative adversarial networks to generate pseudo low monoenergetic CT image from a single-tube voltage CT scanner. (March 2021)

• Record Type: Journal Article
• Authors: Funama, Yoshinori; Oda, Seitaro; Kidoh, Masafumi; Nagayama, Yasunori; Goto, Makoto; Sakabe, Daisuke; Nakaura, Takeshi
• Is Part Of: Physica medica. Volume 83(2021)
• Page Start: 46
• Page End: 51
• Publication Date: 2021-03
• Subjects: Virtual monochromatic image -- Conventional tube-voltage image -- Dual-energy computed tomography -- Conditional generative adversarial networks; Medical physics -- Periodicals; Biophysics -- Periodicals; Biophysics -- Periodicals; Imagerie médicale -- Périodiques; Radiothérapie -- Périodiques; Rayons X -- Sécurité -- Mesures -- Périodiques; Physique -- Périodiques; Médecine -- Périodiques; 610.153
• DOI: 10.1016/j.ejmp.2021.02.015
• Languages: English
• ISSNs: 1120-1797

Abstract

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.