Improved deep convolutional dictionary learning with no noise parameter for low-dose CT image processing

Article type: Research Article

Authors: Wang, Lei^{a; b} | Liu, Yi^{a; b} | Wu, Rui^c | Yan, Rongbiao^{a; b} | Liu, Yuhang^a | Chen, Yang^d | Yang, Chunfeng ^{d; *} | Gui, Zhiguo^{a; b; *}

Affiliations: [a] State Key Laboratory of Dynamic Measurement Technology, North University of China, Taiyuan, China | [b] School of Information and Communication Engineering, North University of China, Taiyuan, China | [c] Shanxi North Xing’an Chemical Industry Co. Ltd., Taiyuan, China | [d] Key Laboratory of Computer Network and Information Integration, Ministry of Education, Southeast University, Nanjing, China

Correspondence: [*] Corresponding authors: Zhiguo Gui, State Key Laboratory of Dynamic Measurement Technology, North University of China, Taiyuan, 030051, China. E-mail: [email protected] and Chunfeng Yang, Key Laboratory of Computer Network and Information Integration, Ministry of Education, Southeast University, Nanjing 211189, China. E-mail: [email protected].

Abstract: Background: Low-Dose computed tomography (LDCT) reduces radiation damage to patients, however, the reconstructed images contain severe noise, which affects doctors’ diagnosis of the disease. The convolutional dictionary learning has the advantage of the shift-invariant property. The deep convolutional dictionary learning algorithm (DCDicL) combines deep learning and convolutional dictionary learning, which has great suppression effects on Gaussian noise. However, applying DCDicL to LDCT images cannot get satisfactory results. Objective:To address this challenge, this study proposes and tests an improved deep convolutional dictionary learning algorithm for LDCT image processing and denoising. Methods:First, we use a modified DCDicL algorithm to improve the input network and make it do not need to input noise intensity parameter. Second, we use DenseNet121 to replace the shallow convolutional network to learn the prior on the convolutional dictionary, which can obtain more accurate convolutional dictionary. Last, in the loss function, we add MSSIM to enhance the detail retention ability of the model. Results:The experimental results on the Mayo dataset show that the proposed model obtained an average value of 35.2975 dB in PSNR, which is 0.2954 –1.0573 dB higher than the mainstream LDCT algorithm, indicating the excellent denoising performance. Conclusion:The study demonstrates that the proposed new algorithm can effectively improve the quality of LDCT images acquired in the clinical practice.

Keywords: Low-dose computed tomography, DenseNet, deep learning, convolutional dictionary learning.

DOI: 10.3233/XST-221358

Journal: Journal of X-Ray Science and Technology, vol. 31, no. 3, pp. 593-609, 2023