Multi-scale Hierarchy Feature Fusion Generative Adversarial Network for Low-Dose CT Denoising

Image noise is an inherent issue in low-dose CT (LDCT). Increasing radiation dose can alleviate this problem to some extent, but it also brings potential risks to the patients. Thus, LDCT denoising has raised increasing attention from researchers. Currently, many deep learning based LDCT denoising methods have been proposed with success, such as encoder-decoder. In this paper, we propose a novel multi-scale hierarchy feature fusion based encoder-decoder network within the GAN frameworkfor LDCT denoising. Specifically, a four-stage multi-scale dilated blocks is introduced to integrate low-level features with high-level features. Comparing with the conventional skip connection, which ignores the semantic gap between low-level features and high-level features, the advantage of our method is the effective use of low-level information. In addition, residual learning is also adopted to boost the training of the network. Experimental results on public dataset have demonstrated the superiority of our method over the state-of-the-art methods under comparison in both visual quality and quantitative evaluation.