Realistic High-resolution Body Computed Tomography Image Synthesis Using Progressive Growing Generative Adversarial Network: A Visual Turing Test.

BACKGROUND Generative adversarial network (GAN)-based synthetic images can be viable solutions to current supervised deep learning challenges. However, generating highly realistic images is a prerequisite for these approaches. OBJECTIVE To investigate and to validate the unsupervised synthesis of highly realistic body computed tomography (CT) images using a progressive growing GAN (PGGAN) trained to learn the probability distribution of normal data. METHODS We trained the PGGAN using 11,755 body CT scans. Ten radiologists then evaluated the results in a binary approach using an independent validation set of 300 images (150 real, 150 synthetic) to judge the authenticity of each image. RESULTS The mean accuracy for ten readers in the entire image set was higher than the random guessing (59.4% vs. 50%, P <0.001). However, in terms of identifying synthetic images as fake, there was no significant difference in specificity between the visual Turing test and random guessing (51.9% vs. 50.0%, P = 0.287). The accuracy between three reader groups with different experience levels was not significantly different (58.0% to 60.5%, P = .36). Inter-reader agreements were poor (κ = 0.11) for the entire image set. In subgroup analysis, the discrepancies between real and synthetic CT images occurred mainly in the thoracoabdominal junction and in anatomical details. CONCLUSIONS The GAN can synthesize highly realistic high-resolution body CT images that are indistinguishable from real images; however, it has limitations in generating body images in the thoracoabdominal junction and lacks accuracy in anatomical details. CLINICALTRIAL