Gradient regularized convolutional neural networks for low-dose CT image enhancement. (21st August 2019)

• Record Type: Journal Article
• Authors: Gou, Shuiping; Liu, Wei; Jiao, Changzhe; Liu, Haofeng; Gu, Yu; Zhang, Xiaopeng; Lee, Jin; Jiao, Licheng
• Is Part Of: Physics in medicine & biology. Volume 64:Number 16(2019:Aug.)
• Publication Date: 2019-08-21
• Subjects: deep learning -- gradient regularization -- image enhancement -- low-dose CT -- convolutional neural network; Biophysics -- Periodicals; Medical physics -- Periodicals; 610.153
• DOI: 10.1088/1361-6560/ab325e
• Languages: English
• ISSNs: 0031-9155

Abstract

Abstract: The potential risks of x-ray to patients have transferred the public's attention from normal dose CT (NDCT) to low-dose CT (LDCT). However, simply lowering the radiation dose of the CT system will significantly degrade the quality of CT images such as noise and artifacts, which compromises the diagnostic performance. Hence, various methods have been proposed to solve this problem over the past decades. Although these methods have achieved impressive results, they also suffer from a drawback of smoothing image details after denoising, which makes it difficult for clinical diagnosis and treatment. To address this issue, this paper introduces a novel gradient regularization method for LDCT enhancement. Rather than common methods which only consider the pixel-wise gray value loss in the reconstruction procedure, we also take the image gradient loss into consideration to preserve image details. By combining the gradient regularization method and the convolutional neural network (CNN) framework, a gradient regularized convolutional neural network (GRCNN) is proposed to enhance LDCT images which has achieved promising performance in our experiments both visually and quantitatively.