title: An overview of fast convergent ordered-subsets reconstruction methods for emission tomography based on the incremental EM algorithm.
abstract: Statistical reconstruction has become popular in emission computed tomography but suffers slow convergence (to the MAP or ML solution). Methods proposed to address this problem include the fast but non-convergent OSEM and the convergent RAMLA [1] for the ML case, and the convergent BSREM [2], relaxed OS-SPS and modified BSREM [3] for the MAP case. The convergent algorithms required a user-determined relaxation schedule. We proposed fast convergent OS reconstruction algorithms for both ML and MAP cases, called COSEM (Complete-data OSEM), which avoid the use of a relaxation schedule while maintaining convergence. COSEM is a form of incremental EM algorithm. Here, we provide a derivation of our COSEM algorithms and demonstrate COSEM using simulations. At early iterations, COSEM-ML is typically slower than RAMLA, and COSEM-MAP is typically slower than optimized BSREM while remaining much faster than conventional MAP-EM. We discuss how COSEM may be modified to overcome these limitations.
authors: Dept. of Medical Imaging and Radiological Sciences, Chang Gung University, 333 Tao-yuan, Taiwan.
authors: Ing-Tsung
authors: IT
authors: Hsiao
authors: Parmeshwar
authors: P
authors: Khurd
authors: Anand
authors: A
authors: Rangarajan
authors: Gene
authors: G
authors: Gindi