Low Dose CT Image Denoising Using Multi-level Feature Fusion Network and Edge Constraints

Low-dose computed tomography image denoising is a challenging task that has been studied by many researchers. Current denoising methods based on deep learning tend to produce a blur effect on the final results, especially at high noise levels, which are prone to over-smoothed edges and loss of details. In this paper, we propose a deep learning approach based on deep convolutional and edge constraints to mitigate these problems. Firstly, to avoid the loss of shallow layers details while obtaining semantically-richer features information, we use dilated convolution instead of standard convolution, and fusion feature maps of different levels to aggregate information from different receptive field. Secondly, in order to improve the network's ability to distinguish between noise and image content, we have designed an attention block to adaptively recalibrate the information relationship of the fusion feature maps. Finally, we incorporate edge prior knowledge into LDCT image denoising task, guiding the network to pay more attention on texture and structure information by edge constraints loss. Extensive experiments demonstrate that the proposed method achieves significant improvements over the state-of-the-art methods.