Abstract—this paper makes a comparison between the slow globally convergent list-mode COSEM and the fast but nonconvergent classical list-mode OSEM methods and produce a way to get a fast and effective COSEM called the enhanced COSEM (ECOSEM). The former globally convergent algorithm is based on the ordered sets expectation maximization algorithm for binned data but has several extensions that make it suitable for two planar detector tomography that are rotating but experience certain limitations. The image estimate measured from the PET scanner is incrementally updated for each coincidence event measured. The events obtained are used as soon as possible, that improves the current image estimate, and therefore the convergence speed towards the maximum-likelihood is accelerated. We provide a simulated result to overcome those limitations and to reconstruct the image with maximum-likelihood.

Index Terms—Maximum-likelihood (ML), positron emission tomography (PET), row-action maximum-likelihood algorithm (RAMLA), expectation-maximization (EM), event-by-event (EBE), ordered subsets (OS), Bayesian, filtered back projection (FBP), maximum a-posteriori (MAP), penalized weighted least-squares (PWLS).

S. Vimalraj is the lecturer of EEE Department, VLB Janakiammal College of Engineering and Technology, Coimbatore, India. E-mail: vimalrajeee@gmail.com
V. Dhineshkumar is currently doing his Bachelor’s degree in Electrical and Electronics Engineering, VLB Janakiammal College of Engineering and Technology, Coimbatore, India. E-mail: dhinesh89@gmail.com
S. Gobhinath is currently doing his Bachelor’s degree in Electrical and Electronics Engineering, VLB Janakiammal College of Engineering and Technology, Coimbatore, India. E-mail: s.gobhinath@gmail.com

Cite: V. Dhineshkumar, S. Gobhinath, and S. Vimalraj, "Analysis of Various Algorithms and Provide a Graph Theory Approach towards Speeding of Image Reconstruction with Reduced Iterations," International Journal of Computer and Electrical Engineering vol. 3, no. 4, pp. 561-565, 2011.