Optimization for limited angle tomography in medical image processing

This paper aims to reduce the problems of incomplete data in computed tomography, which happens frequently in medical image process and analysis, e.g., when the high-density region of objects can only be penetrated by X-rays at a limited angular range. As the projection data are available only in an angular range, the incomplete data problem can be attributed to the limited angle problem, which is an ill-posed inverse problem. Image reconstruction based on total variation (TV) reduces the problem and gives better performance on edge-preserving reconstruction; however, the artificial parameter can only be determined through considerable experimentation. In this paper, an effective TV objective function is proposed to reduce the inverse problem in the limited angle tomography. This novel objective function provides a robust and effective reconstruction without any artificial parameter in the iterative processes, using the TV as a multiplicative constraint. The results demonstrate that this reconstruction strategy outperforms some previous ones.