A novel alternative algorithm for limited angle tomography
This paper studies incomplete data problems of circular cone-beam computed tomography, which occur frequently in medical imaging and industrial imaging. The incomplete data problems in which projection data are only available in an angular range can be attributed to the limited angle tomography. Limited angle tomography is a severely ill-posed inverse problem. In recent years, image reconstruction based on total variation (TV) was employed to reduce the problem and gave better performance on edge-preserving reconstruction. However, the artificial parameter can only be determined through considerable experimentation. In this paper, an alternating minimization method based on TV is proposed to reduce the data insufficiency in tomographic imaging. This novel alternating minimization method provides a robust and effective reconstruction without any artificial parameter in the iterative processes, by using the TV as a multiplicative constraint. The results demonstrate that this new reconstruction method brings satisfactory performance.