Improvements of the ML-EL-algorithm for reconstruction of positron emission tomography images

Positron emission tomography (PET) is a technique that has opened new facilities to study the metabolic activity of the human body. In the last years many algorithms have been developed for reconstructing tomography images. The often used maximum likelihood expectation maximization algorithm (ML-EM) seems to be a stable method and was developed by Shepp and Vardi in 1982. However, the ML-EM algorithm causes some serious problems in the context of the application considered. It is an iterative procedure and converges to a stationary point, however, the reconstructed image seems to be distorted by superposed high frequency noise. It is shown, that the ML-EM-algorithm is not based on significant statistical properties in our problem, which has been verified by investigations. As a consequence of these results the algorithm has been modified in two ways. First, the expectation step has been replaced by a deterministic algorithm with accelerated convergence behaviour. Second, prior information is used to improve the statistical performance of the algorithm. Consequently, the algorithm has been changed to a maximum a posteriori estimator.