Bayesian MAP restoration of scintigraphic images

We are interested in the problem of restoring scintigraphic images acquired by the gamma detector in nuclear medicine. The aim is to improve the detectability of possible heterogeneous areas in different organs. We propose to solve the problem in the Bayesian framework with the Maximum A Posteriori (MAP) principle. The prior information was modeled by a Markov Random Field (MRF). The optimization is based on two kinds of methods: the stochastic algorithm of simulated annealing with a Gibbs sampler, and the deterministic algorithm of Graduated Non Convexity (GNC). We compared the results to the images restored by the Metz filter, more classical in this field. We applied these methods to the restoration of cold or warm nodules in the thyroid gland. We noticed the superiority of the proposed methods in terms of contrast around the nodules and uniformity in the images.