A Denoising Algorithm for CT Image Using Low-rank Sparse Coding

• Type of Material: Article
• Authors: Yang Lei, Emory University; Dong Xu, Zhejiang Cancer Hospital; Zhengyang Zhou, Emory University; Tonghe Wang, Emory University; Xueyuan Dong, Emory University; Tian Liu, Emory University; Anees Dhabaan, Emory University; Walter J Curran, Emory University; Xiaofeng Yang, Emory University
• Language: English
• Date: 2018-03-20
• Publisher: Society of Photo-optical Instrumentation Engineers (SPIE)
• Publication Version: Final Published Version
• Copyright Statement: © (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
• Final Published Version (URL): https://www.spiedigitallibrary.org/conference-proceedings-of-spie/10574/2292890/A-denoising-algorithm-for-CT-image-using-low-rank-sparse/10.1117/12.2292890.short
• Title of Journal or Parent Work: Proceedings of SPIE
• ISSN: 0277-786X
• Issue: 10574
• Grant/Funding Information: This research is supported in part by the National Cancer Institute of the National Institutes of Health under Award Number R01CA215718; the Department of Defense (DoD) Prostate Cancer Research Program (PCRP) Award W81XWH-13-1-0269; and Dunwoody Golf Club Prostate Cancer Research Award, a philanthropic award provided by the Winship Cancer Institute of Emory University.
• Keywords: denoising; CT; sparse coding
• Research Categories: Health Sciences, Radiology; Physics, Radiation

Abstract

We propose a denoising method of CT image based on low-rank sparse coding. The proposed method constructs an adaptive dictionary of image patches and estimates the sparse coding regularization parameters using the Bayesian interpretation. A low-rank approximation approach is used to simultaneously construct the dictionary and achieve sparse representation through clustering similar image patches. A variable-splitting scheme and a quadratic optimization are used to reconstruct CT image based on achieved sparse coefficients. We tested this denoising technology using phantom, brain and abdominal CT images. The experimental results showed that the proposed method delivers state-of-art denoising performance, both in terms of objective criteria and visual quality.