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Sparse-view computed tomography image reconstruction via a combination of L1 and SL0 regularization

Issue title: Frontiers in Biomedical Engineering and Biotechnology – Proceedings of the 4th International Conference on Biomedical Engineering and Biotechnology, 18–21 August 2015, Shanghai, China

Guest editors: Feng Liu, Dong-Hoon Lee, Ricardo Lagoa and Sandeep Kumar

Article type: Research Article

Authors: Qi, Hongliang | Chen, Zijia | Guo, Jingyu | Zhou, Linghong; *

Affiliations: Institute of Medical Instruments, School of Biomedical Engineering, Southern Medical University, 510515, Guangzhou, China

Correspondence: [*] Address for correspondence: Linghong Zhou, Institute of Medical Instruments, School of Biomedical Engineering, Southern Medical University, 510515, Guangzhou, China. Tel.: +86 20 61648291; Fax: +86 20 62789510; E-mail: [email protected].

Keywords: Sparse-view reconstruction, smoothed L0 regularization, L1 regularization, total variation

DOI: 10.3233/BME-151437

Journal: Bio-Medical Materials and Engineering, vol. 26, no. s1, pp. S1389-S1398, 2015

Published: 2015
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Low-dose computed tomography reconstruction is an important issue in the medical imaging domain. Sparse-view has been widely studied as a potential strategy. Compressed sensing (CS) method has shown great potential to reconstruct high-quality CT images from sparse-view projection data. Nonetheless, low-contrast structures tend to be blurred by the total variation (TV, L1-norm of the gradient image) regularization. Moreover, TV will produce blocky effects on smooth and edge regions. To overcome this limitation, this study has proposed an iterative image reconstruction algorithm by combining L1 regularization and smoothed L0 (SL0) regularization. SL0 is a smooth approximation of L0 norm and can solve the problem of L0 norm being sensitive to noise. To evaluate the proposed method, both qualitative and quantitative studies were conducted on a digital Shepp-Logan phantom and a real head phantom. Experimental comparative results have indicated that the proposed L1/SL0-POCS algorithm can effectively suppress noise and artifacts, as well as preserve more structural information compared to other existing methods.

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