Revisiting stopping rules for iterative methods used in emission tomography

The expectation maximization algorithm is commonly used to reconstruct images obtained from positron emission tomography sinograms. For images with acceptable signal to noise ratios, iterations are terminated prior to convergence. A new quantitative and reproducible stopping rule is designed and validated on simulations using a Monte-Carlo generated transition matrix with a Poisson noise distribution on the sinogram data. Iterations are terminated at the solution which yields the most probable estimate of the emission densities while matching the sinogram data. It is more computationally efficient and more accurate than the standard stopping rule based on the Pearson's χ2 test.