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Relaxation is widely recognized as a useful tool for providing convergence in block-iterative algorithms , , . In the present article we give new results on the convergence of RAMLA (Row Action Maximum Likelihood Algorithm) , filling some important theoretical gaps. Furthermore, because RAMLA and OS-EM (Ordered Subsets - Expectation Maximization)  are the algorithms for statistical reconstruction currently being used in commercial emission tomography scanners, we present a comparison between them from the viewpoint of a specific imaging task. Our experiments show the importance of relaxation to improve image quality.