On the convergence of an EM-type algorithm for penalized likelihood estimation in emission tomography

Recently, we proposed an extension of the expectation maximization (EM) algorithm that was able to handle regularization terms in a natural way. Although very general, convergence proofs were not valid for many possibly useful regularizations. We present here a simple convergence result that is valid assuming only continuous differentiability of the penalty term and can be also extended to other methods for penalized likelihood estimation in tomography.