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Chin. Phys. B, 2012, Vol. 21(10): 108703    DOI: 10.1088/1674-1056/21/10/108703
INTERDISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY Prev   Next  

A Compton scattering image reconstruction algorithm based on total variation minimization

Li Shou-Peng, Wang Lin-Yuan, Yan Bin, Li Lei, Liu Yong-Jun
National Digital Switching System Engineering & Technology Research Center, Zhengzhou 450002, China
Abstract  Compton scattering imaging is a novel radiation imaging method using scattered photons. Its main characteristics are detectors that do not have to be on the opposite side of the source, so avoiding the rotation process. The reconstruction problem of Compton scattering imaging is the inverse problem to solve electron densities from nonlinear equations, which is ill-posed. This means the solution exhibits instability and sensitivity to noise or erroneous measurements. Using the theory for reconstruction of sparse images, a reconstruction algorithm based on total variation minimization is proposed. The reconstruction problem is described as an optimization problem with nonlinear data-consistency constraint. The simulated results show that the proposed algorithm could reduce reconstruction error and improve image quality, especially when there are not enough measurements.
Keywords:  Compton scattering tomography      inverse problem      image reconstruction      sparse      total variation  
Received:  29 February 2012      Revised:  09 April 2012      Published:  01 September 2012
PACS:  87.57.nf (Reconstruction)  
  83.85.Hf (X-ray and neutron scattering)  
  06.30.Dr (Mass and density)  
Fund: Project supported by the National Basic Research Program of China (Grant No. 2011CB707701) and the National High Technology Research and Development Program of China (Grant Nos. 2009AA012200 and 2012AA011603).
Corresponding Authors:  Yan Bin     E-mail:  tom.yan@gmail.com

Cite this article: 

Li Shou-Peng, Wang Lin-Yuan, Yan Bin, Li Lei, Liu Yong-Jun A Compton scattering image reconstruction algorithm based on total variation minimization 2012 Chin. Phys. B 21 108703

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