Image reconstruction by an alternating minimisation

  • Authors:
  • Xiaoqiang Lu;Yi Sun;Yuan Yuan

  • Affiliations:
  • Department of electrical engineering at Dalian University of Technology, Dalian, P.R. China;Department of electrical engineering at Dalian University of Technology, Dalian, P.R. China;Center for OPTical IMagery Analysis and Learning (OPTIMAL), State Key Laboratory of Transient Optics and Photonics, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, ...

  • Venue:
  • Neurocomputing
  • Year:
  • 2011

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Abstract

This paper focuses on the problem of incomplete data in the applications of the circular cone-beam computed tomography. This problem is frequently encountered in medical imaging sciences and some other industrial imaging systems. For example, it is crucial when the high density region of objects can only be penetrated by X-rays in a limited angular range. As the projection data are only available in an angular range, the above mentioned incomplete data problem can be attributed to the limited angle problem, which is an ill-posed inverse problem. This paper reports a modified total variation minimisation method to reduce the data insufficiency in tomographic imaging. This proposed method is robust and efficient in the task of reconstruction by showing the convergence of the alternating minimisation method. The results demonstrate that this new reconstruction method brings reasonable performance.