Improvement of reconstructed images in ordered subset-Bayesian reconstruction method

In ordered subsets-expectation maximization (OS-EM) the projection data are grouped into subsets of projection data. The OS algorithm can also be applied to the maximum a posteriori (MAP) method. We call it the OS-Bayesian Reconstruction (BR) method. Generally, the OS algorithm uses a fixed number of projections, so called “subset levels”, and the recovered frequency components of a reconstructed image depends upon the number of projections in a subset. We propose a new method named MOS (Modified OS)-BR which modifies the number of projections for each iteration step in an OS-BR algorithm. We compared the MOS-BR with MAP-EM and OS-BR. From the results the mean absolute error was decreased stably with MOS-BR and the proposed method was extremely effective when the projection data included noise