Image oscillation reduction and convergence acceleration for OS-EM reconstruction [PET imaging]

Investigates the use of two approaches to reduce the image oscillation of OS-EM (ordered subset expectation maximization) reconstruction that is due to the inconsistencies among different partial subsets of the projection measurements (the sinogram) when considering as a group. One approach pre-processes the sinogram to make it satisfy a sinogram consistency condition. The second approach takes the average of the intermediary images (i.e. it smoothes image values over sub-iterations). Both approaches were found to be capable of reducing the image oscillation, and a combination of both was most effective. With these approaches, the convergence of OS-EM reconstruction is further improved. For computer-simulated data and real PET data, a single iteration of this new OS-EM reconstruction was shown to yield images comparable to those with 80 EM iterations.