PET image reconstruction using simulated annealing

In positron emission tomography (PET) images have to be reconstructed from noisy projection data. The noise on the PET data can be modeled by a Poisson distribution. The development of statistical (iterative) reconstruction techniques addresses the problem of noise. In this paper we present the results of introducing the simulated annealing technique as a statistical reconstruction algorithm for PET. We have successfully implemented a reconstruction algorithm based upon simulated annealing, with paying particular attention to the fine-tuning of various parameters (cooling schedule, granularity, stopping rule, ...). In addition, we have developed a cost function more appropriate to the noise statistics (e.g. Poisson) and the reconstruction method (e.g. ML). The comparison with other reconstruction methods using computer phantom studies proves the potential power of the simulated annealing technique for the reconstruction of PET-images.