DRGAN: a deep residual generative adversarial network for PET image reconstruction

Abstract

Positron emission tomography (PET) image reconstruction from low-count projection data and physical effects is challenging because the inverse problem is ill-posed and the resultant image is usually noisy. Recently, generative adversarial networks (GANs) have also shown their superior performance in many computer vision tasks and attracted growing interests in medical imaging. In this work, the authors proposed a novel model [deep residual generative adversarial network (DRGAN)] based on GANs for the reduction of streaking artefacts and the improvement of PET image quality. An innovative feature of the proposed method is that the authors trained a generator to produce `residual PET map' (RPM) for image representation, rather than generate PET images directly. DRGAN used two discriminators (critics) to enforce anatomically realistic PET images and RPM. To better boost the contextual information, the authors designed residual dense connections followed with pixel shuffle operations (RDPS blocks) that encourage feature reuse and prevent losing resolution. Both simulation data and real clinical PET data are used to evaluate the proposed method. Compared with other state-of-the-art methods, the quantification results show that DRGAN can achieve better performance in bias-variance trade-off and provide comparable image quality. Their results were rigorously evaluated by one radiologist at the Shanxi Cancer Hospital.