Scale-Sensitive Generative Adversarial Network for Low-Dose CT Image Denoising

Given the escalating potential risk associated with X-ray radiation exposure to patients, scholars have been dedicated to investigating advanced algorithms for low-dose CT (LDCT) imaging. The utilization of Generative Adversarial Networks (GANs) has gained significant traction in the field of LDCT denoising research over the past few years, owing to its notable performance advantages. However, variations in the distribution intensity of noise and artifacts in the LDCT image can compromise the robustness and denoising performance of GAN. In this paper, we propose a scale-sensitive generative adversarial network to enhance the robustness of GAN used in LDCT denoising. Our proposed network incorporates an error feedback pyramid generator to improve the denoising network's sensitivity towards noise and artifacts present in LDCT images by performing feature extraction and noise reduction at different scales. Additionally, we introduce a multi-scale residual discriminator with a wider receptive field and stronger feature extraction ability to enhance the discrimination between true and false samples. Furthermore, specific constraints are incorporated into the loss functions based on the network structure, thereby enhancing the effect of adversarial training between the error feedback pyramid generator and multi-scale residual discriminator while improving the performance of the denoising network. Experimental results demonstrate that our proposed network outperforms state-of-the-art methods in terms of artifact suppression, structural preservation, and model generalization capability for LDCT denoising.