DFSNE-Net: Deviant feature sensitive noise estimate network for low-dose CT denoising

Abstract

Background: Computed tomography (CT) has radiation problems, and high-quality CT scanning is usually accompanied by high doses of radiation that can be harmful to humans, so low-dose CT denoising has received extensive academic attention.

Method: In this paper, firstly, the concept of deviant features is proposed which lead to the hypothesis of correlation between noise and deviant features. Secondly, in order to estimate the noise in CT, a new method of deviant feature perception and downsampling is proposed. Specifically, the deviant feature perception module based on the multi-scale convolutional cooperative (MSC-DFPM) and Filtering module based on the self-information space attention(SISA-FM) are proposed, and construct the deviant feature sensitive noise estimate network (DFSNE-Net), then a balanced loss function and training strategy adapted to the DFSNE-Net are proposed. Finally, noise distribution normalization based on skewness and kurtosis(SK-NDN) and low credible noise suppression based on confidence interval(CI-LCNS) as the noise optimization methods are proposed to optimize the estimated noise of DFSNE-Net, which is applied to the denoising task of CT.

Conclusions: Experiments and results demonstrate that our proposed denoising method in this paper obtains a better denoising effect than the current state-of-the-art denoising methods in different evaluation indicator, which proves the hypothesis that noise is strongly correlated with deviant features. It is also proved that the denoising method proposed in this paper can achieve the denoising task for different doses of CT.

Keywords: Attention; Deep learning; Denoise; Generative adversarial networks; Low-dose CT; Multi-scale convolution.