A Bayesian method is described for solving problems in image reconstruction from projections. The approach utilizes a Gibbs distribution to incorporate a priori spatial information. The model has been used for reconstruction of simulated data, data from phantom studies, and real scanning data. The results indicate that the technique has the potential to provide both improvement in spatial resolution and reduction of noise.
