Bayesian Reconstruction for Emissiom Tomography via Deterministic Annealing

In emission tomography, a principled means of incorporating a piecewise smooth prior on the source f is via a mixed variable objective function E(f; l) deened on f and binary valued line processes l. MAP estimation on E(f; l) results in the diicult problem of minimizing an objective function that includes a nonsmooth Gibbs prior deened on the spatial derivatives of f. Previous approaches have used heuristic Gibbs potentials that incorporate line processes, but only approximately. In this work, we present a continuation method in which the correct function is approached through a sequence of smooth functions. Our continuation method is implemented using a GEM{ICM procedure. Simulation results show improvement using our continuation method relative to using alone, and to conventional EM reconstructions. Finally, we show a means of generalizing this formalism to the less restrictive case of piecewise linear instead of piecewise at priors.