Accelerated MAP reconstructions using an accelerated factor

In emission tomography, statistical reconstruction has been proved outperforming FBP (filtered backprojection). ML-EM (maximum likelihood expectation-maximization) algorithm is one of the most widely used statistical reconstruction methods. Although with good image quality, the ML-EM algorithm is challenged by its slow convergence speed and ill-condition problem. To solve for the problems, based on the ideas from the accelerated EM (AEM) and maximum a posteriori EM (MAP-EM) algorithms, we derived two approaches (MAP-AEM1 and MAP-AEM2) in this study to accelerate MAP reconstruction using a bigger step size instead of ordered subsets. The results were compared with ordered-subset methods of both BSREM and MAPCOSEM. From the preliminary results, our methods showed the speedup ability for the traditional MAP-EM.