Electronic cleansing in photon-counting CT colonography by use of self-supervised 3D-GAN

Abstract

Photon-counting CT is an emerging technology with several advantages over conventional CT technology, such as the ability to reduce radiation exposure to CT. In this study, we evaluated the effect of the use of photon-counting CT colonography on the performance of our self-supervised 3D generative adversarial learning (GAN)-based electronic cleansing (EC) scheme. We simulated a fecal-tagging CT colonography case by use of an anthropomorphic colon phantom. The empty phantom served as the ground truth for the EC. Both the empty and fecal-tagging versions of the phantom were scanned by use of a photon-counting CT and a conventional CT scanner. We evaluated the performance of the EC scheme by using 100 paired volumes of interest extracted from the corresponding locations on the empty and fecal-tagging phantoms that had not been used for the training of the EC scheme. The peak signal-to-noise ratio was used as the metric for the quality of the EC images generated. Our preliminary results indicate that using photon-counting CT colonography at a low dose generates higher-quality EC images than those obtained by using conventional CT colonography. The results also demonstrate that our self-supervised training scheme generates images of higher quality than those obtained by use of conventional supervised training. Therefore, photon-counting CT colonography combined with our self-supervised 3D-GAN EC scheme is expected to provide EC images of the highest quality in low-dose fecal-tagging CT colonography.