Accelerated iterative reconstruction based on the maximum a posteriori expectation maximization

It is demonstrated that the ML-EM (maximum-likelihood expectation-maximization) algorithm is a particular case of the modified Newton method whose convergence is proved and can be optimally accelerated by an overrelaxation parameter. In order to overcome the checkerboard effect, this accelerated ML-EM algorithm can be penalized with a Gaussian a priori distribution in the framework of a MAP (maximum a posteriori) approach. The experimental results obtained here indicate that significant savings in computation time may be achieved using the accelerated MAP algorithm.<
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