Image quality in the modified ordered subset-Bayesian reconstruction

Iterative image reconstruction methods such as the maximum likelihood-expectation maximization (ML-EM) method and the maximum a posteriori (MAP)-EM method can be accelerated by introducing an ordered subsets (OS) algorithm in which the projection data are grouped into subsets. We call the OS algorithm applied to the MAP method the OS-(Bayesian reconstruction)BR. Because the OS algorithm uses a fixed number of projections in a subset, the quality of images depends upon the number of projections. To improve the quality of the image we have proposed a new method, named MOS(Modified OS)-BR, which modifies the number of projections for each iteration step in an OS-BR algorithm. In this paper we evaluate the sequence to increase the number of projections and compare images reconstructed by MOS-BR with those by ML-EM, MAP-EM, OS-EM and OS-BR. In view of the results the proposed method is applicable to clinical data