Dilated Residual Convolutional Neural Networks for Low-Dose CT Image Denoising

• Published in: 2020 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS) pp. 189 - 192
• Main Authors: Thanh Trung, Nguyen, Trinh, Dinh-Hoan, Linh Trung, Nguyen, Thi Thuy Quynh, Tran, Luu, Manh-Ha
• Format: Conference Proceeding
• Language: English
• Published: IEEE 08-12-2020
• Subjects: Computed tomography; Computer tomography; convolutional neural network; dilated residual network; Image denoising; Image reconstruction; low dose imaging; medical image denoising; Noise reduction; perceptual loss; Residual neural networks; Training; X-ray imaging
• DOI: 10.1109/APCCAS50809.2020.9301693

X-ray computed tomography (CT) imaging, which uses X-ray to acquire image data, is widely used in medicine. High X-ray doses may be harmful to the patient's health. Therefore, X-ray doses are often reduced at the expense of reduced quality of CT images. This paper presents a convolutional neural network model for low-dose CT image denoising, inspired by a recently introduced dialated residual network for despeckling of synthetic aparture radar images (SAR-DRN). In particular, batch normalization is added to some layers of SAR-DRN in order to adapt SAR-DRN for low-dose CT denoising. In addition, a preprocessing layer and a post-processing one are added in order to improve the receptive field and to reduce computational time. Moreover, the perceptual loss combined with MSE one are used in the training phase so that the proposed denoising model can preserve more subtle details of denoised images. Experimental results show that the proposed model can denoise low-dose CT images efficiently as compared to some state-of-the-art methods.