International Journal of Radiation Research

en Generation of computed tomography images with different tube voltages by generative adversarial network Radiation Biology Radiation Biology تحقيق بديع Original Research <div style="text-align: justify;">Background: Single-energy computed tomography (SECT) requires different energies for disease diagnosis and detection. The patient must be scanned again after the CT scan to obtain other tube-voltage images, causing increased radiation exposure. One method for avoiding rescanning is virtual monochromatic images (VMI) by dual-energy computed tomography (DECT). However, VMI has not proven superior to SECT. Moreover, DECT is not available at all facilities. This study used generative adversarial networks to generate 120 kVp and Sn140 kVp CT images from 80 kVp CT images. Material and Methods: This study involved 35 patients with pulmonary hypertension who underwent CT scans with DECT. The peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) were calculated to evaluate the difference between the real and pseudo images. Furthermore, the mean CT values of the pulmonary arteries (PA) were compared. Results: The SSIM was 0.99 ± 9.15×10-5 and 0.99 ± 3.38×10-3 at 120 kVp and Sn140 kVp, respectively. The PSNR was 69.1 ± 1.29 and 66.5 ± 5.40 at 120 kVp and Sn140 kVp, respectively. Additionally, no significant differences were observed in the mean CT values of PA (p > 0.05) between the real and pseudo images. Conclusion: The proposed model accurately generated 120 kVp and Sn140 kVp CT images from 80 kVp CT images.</div> Deep learning, medical image processing, dual-energy scanned projection radiography, pulmonary artery. 873 879 http://ijrr.com/browse.php?a_code=A-10-1-1409&slc_lang=en&sid=1 H. Takano hirokazu.takano.a1@tohoku.ac.jp 7900319475328460032173 7900319475328460032173 Yes Department of Radiological Technology, Tohoku University Hospital, 1-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8574, Japan Y. Kondo 7900319475328460032174 7900319475328460032174 No Graduate School of Health Sciences, Niigata University, 746 Asahimachidori 2bancho, Chuo-ku, Niigata City, Niigata, 951-8518, Japan Y. Ishizawa 7900319475328460032175 7900319475328460032175 No Department of Radiological Technology, Tohoku University Hospital, 1-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8574, Japan S. Onodera 7900319475328460032176 7900319475328460032176 No Department of Radiological Technology, Tohoku University Hospital, 1-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8574, Japan M. Saito 7900319475328460032177 7900319475328460032177 No Graduate School of Health Sciences, Niigata University, 746 Asahimachidori 2bancho, Chuo-ku, Niigata City, Niigata, 951-8518, Japan H. Ota 7900319475328460032178 7900319475328460032178 No Department of Diagnostic Radiology, Tohoku University Hospital, 1-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8574, Japan