Abstract No. 597 Improved perceived image quality and operator comfort when interpreting DynaCT images using variants of super resolution generative adversarial networks

Abstract

Purpose is to improve perceived image quality and operator comfort when interpreting DynaCT images, we propose using variants of super resolution generative adversarial networks (GANs). Based on multiple factors of image acquisition, DynaCT images can have less perceived image quality than desired by human observers and interpreters even if these images may still contain all necessary diagnostic information at the spatial and contrast resolutions at which they are collected. Therefore, in order to improve perceived image quality and operator comfort when interpreting DynaCT images, we propose using variants of super resolution generative adversarial networks (GANs) in order to improve the perceptual quality of DynaCT images. We utilized multiple public versions of super resolution GANs that are available for local download on GitHub such as ESRGAN. We will also use a public repository of CT images (the CHAOS dataset) in order to train our own super resolution GAN variants. These variants of super resolution GANs will then be applied to locally collected DynaCT data and our research team will review the images for perceptual quality and neural network induced artifacts. All optimized images were subjectively perceived as of higher quality and higher resolution by two independent interpreting radiologists. There CT images had findings that were better visualized on optimized images including fistula formation for an abscess evaluation, additional arterial supply and, hepatic venous structures. Diagnostic CT’s and MRI diagnosed showed these findings before the procedure. Perceived image quality and operator comfort of DynaCT images could be increased using super resolution generative adversarial networks (GANs).