RESEARCH PROGRESS OF DEEP LEARNING IN LOW-DOSE CT IMAGE DENOISING
Fan Zhang, Jingyu Liu, Ying Liu, Xinhong Zhang
Abstract
Low-dose computed tomography (CT) will increase noise and artefacts while reducing the radiation dose, which will adversely affect the diagnosis of radiologists. Low-dose CT image denoising is a challenging task. There are essential differences between the traditional methods and the deep learning-based methods. This paper discusses the denoising approaches of low-dose CT image via deep learning. Deep learning-based methods have achieved relatively ideal denoising effects in both subjective visual quality and quantitative objective metrics. This paper focuses on three state-of-the-art deep learning-based image denoising methods, in addition, four traditional methods are used as the control group to compare the denoising effect. Comprehensive experiments show that the deep learning-based methods are superior to the traditional methods in low-dose CT images denoising.