Learning a Frequency Separation Network with Hybrid Convolution and Adaptive Aggregation for Low-dose CT Denoising
Xingyao Jiang, Lulu Wang, Zhongshi He, Jinglong Du · 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · 2021
Low-dose computed tomography (CT) has attracted widespread attention in the medical imaging field due to its mild radiation hazards to the human body. However, the image may suffer from unpleasing noises under the low-dose radiation condition, which is not conducive to accurate analysis and diagnosis of diseases. Recently, deep learning has shown great potential in low-dose CT denoising. However, current approaches neglect that noise causes varying degrees of damage to low-/high-frequency components of the LDCT image, which hinders further improvement in denoising accuracy. In this paper, we propose a novel frequency separation network (FSNet) for low-dose CT image denoising, which recovers low-/high-frequency components separately, and takes full advantage of them for reconstructing high-quality CT images. To recover clean frequency components effectively, we design hybrid convolution module (HCM) that exploits parallel cascaded convolution path and encoder-decoder path to eliminate noises and preserve image structures. To fuse clean frequency components adaptively, we introduce content attention module (CAM) to adjust the contribution of features across valuable channels and regions, which encourages FSNet to restore image contents based on their frequency characteristics. Extensive experimental results on the Mayo Clinic low-dose CT image dataset show that our proposed FSNet outperforms state-of-the-art denoising methods.