  We study the maximum likelihood model in emission tomography and propose a
new family of algorithms for its solution, called String-Averaging
Expectation-Maximization (SAEM). In the String-Averaging algorithmic regime,
the index set of all underlying equations is split into subsets, called
"strings," and the algorithm separately proceeds along each string, possibly in
parallel. Then, the end-points of all strings are averaged to form the next
iterate. SAEM algorithms with several strings presents better practical merits
than the classical Row-Action Maximum-Likelihood Algorithm (RAMLA). We present
numerical experiments showing the effectiveness of the algorithmic scheme in
realistic situations. Performance is evaluated from the computational cost and
reconstruction quality viewpoints. A complete convergence theory is also
provided.
