Non-uniformity correction for photon-counting detectors using double GANs

Abstract

The development of energy-resolving photon-counting detectors provides a new approach for obtaining spectral information in computed tomography. However, the non-uniformity between different photon-counting detector pixels can cause stripe artefacts in projection domain and concentric ring artefacts in image domain. Here we propose a nonuniformity correction method based on two generative adversarial nets (GANs). The first GAN is a conditional GAN and is responsible for ring artifacts estimation in image domain. The first GAN is trained on 2016 AAPM Grand Challenge dataset, with ring artifacts artificially introduced. The second GAN is an ordinary GAN and is responsible for experimental ring artifacts auto-modeling. The second GAN is trained on real ring artifacts removed from experimental images by the first GAN and aims to provide ample and realistic training labels for re-training the first GAN. Experimental results show that GAN can accurately extract the characteristics of ring artefacts and get them removed from original images to get clear images. Besides, GAN can also be used for realistic training label generation thus better improve the performance of ring artifacts estimation network on experimental datasets.