2020 年 37 巻 2 号 p. 21-27
In this study, we evaluated the effect of a newly developed super-resolution processing using Deep Convolutional Neural Network (DCNN) on the images processed with the conventional dynamic processing in digital radiography (DR). We used human phantom images to obtain case samples of which each image included a lateral view of thoracolumbar junction. All case samples were processed without and with dynamic processing, which were ranging from 1 to 4 steps of image enhancement processing. Deep Denoising Super Resolution (DDSR) were trained and assessed using the supervised image (the original image) and the low-resolution image degraded to 1/3 of matrix size by binning from the original image. The effects of the DDSR on the conventional dynamic processing were assessed using the modified Ura method of Scheffe's paired comparison. The average psychological measures for interpreting vertebral body of thoracolumbar junction tended to increase as the enhancement of dynamic processing increased, regardless of the application of DDSR. The correlation between the average psychological measures without and with DDSR was very high with a correlation coefficient of 0.98. We conclude that the effect of the DDSR on the conventional dynamic processing would be neglected on the observation of a diagnosis object in DR.