Low-mAs X-ray CT image reconstruction by adaptive-weighted TV-constrained penalized re-weighted least-squares

Article type: Research Article

Authors: Liu, Yan^; | Ma, Jianhua | Zhang, Hao^; | Wang, Jing | Liang, Zhengrong^;

Affiliations: Department of Radiology, State University of New York, Stony Brook, NY, USA | Department of Electrical and Computer Engineering, State University of New York, Stony Brook, NY, USA | School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China | Department of Radiology and Biomedical Engineering, State University of New York, Stony Brook, NY, USA | Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA

Note: [] Corresponding author: Zhengrong Liang, Departments of Radiology and Biomedical Engineering, State University of New York, Stony Brook, NY, USA. Tel.: +1 631 444 7837; Fax: +1 631 444 6450; E-mail: Jerome@mil.sunysb.edu

Abstract: BACKGROUND: The negative effects of X-ray exposure, such as inducing genetic and cancerous diseases, has arisen more attentions. OBJECTIVE: This paper aims to investigate a penalized re-weighted least-square (PRWLS) strategy for low-mAs X-ray computed tomography image reconstruction by incorporating an adaptive weighted total variation (AwTV) penalty term and a noise variance model of projection data. METHODS: An AwTV penalty is introduced in the objective function by considering both piecewise constant property and local nearby intensity similarity of the desired image. Furthermore, the weight of data fidelity term in the objective function is determined by our recent study on modeling variance estimation of projection data in the presence of electronic background noise. RESULTS: The presented AwTV-PRWLS algorithm can achieve the highest full-width-at-half-maximum (FWHM) measurement, for data conditions of (1) full-view 10 mA acquisition and (2) sparse-view 80 mA acquisition. In comparison between the AwTV/TV-PRWLS strategies and the previous reported AwTV/TV-projection onto convex sets (AwTV/TV-POCS) approaches, the former can gain in terms of FWHM for data condition (1), but cannot gain for the data condition (2). CONCLUSIONS: In the case of full-view 10 mA projection data, the presented AwTV-PRWLS shows potential improvement. However, in the case of sparse-view 80 mA projection data, the AwTV/TV-POCS shows advantage over the PRWLS strategies.

Keywords: X-ray computed tomography, low-mAs protocol, penalized re-weighted least-squares, adaptive weighted total variation, projection onto convex sets

DOI: 10.3233/XST-140437

Journal: Journal of X-Ray Science and Technology, vol. 22, no. 4, pp. 437-457, 2014