An iterative algorithm for computed tomography image reconstruction from limited-angle projections
This work addresses a practical issue in tomography imaging for applications where full-angle data is unavailable, but it appears incremental as it builds on existing iterative methods with modifications.
The paper tackles the problem of computed tomography image reconstruction from noisy limited-angle projections by developing an iterative reprojection-reconstruction algorithm based on a modified Papoulis-Gerchberg scheme, which improves reconstruction quality as demonstrated in simulations.
In application of tomography imaging, limited-angle problem is a quite practical and important issue. In this paper, an iterative reprojection-reconstruction (IRR) algorithm using a modified Papoulis-Gerchberg (PG) iterative scheme is developed for reconstruction from limited-angle projections which contain noise. The proposed algorithm has two iterative update processes, one is the extrapolation of unknown data, and the other is the modification of the known noisy observation data. And the algorithm introduces scaling factors to control the two processes, respectively. The convergence of the algorithm is guaranteed, and the method of choosing the scaling factors is given with energy constraints. The simulation result demonstrates our conclusions and indicates that the algorithm proposed in this paper can obviously improve the reconstruction quality.