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The effects of several of Gibbs prior distributions in terms of noise characteristics, edge sharpness, and overall quantitative accuracy of the final estimates obtained from an iterative maximum a posteriori (MAP) procedure applied to data from a realistic chest phantom are demonstrated. The effects of the adjustable parameters built into the prior distribution on these properties are examined. It is found that these parameter values influence the noise and edge characteristics of the final estimate and can generate reconstructions closer to the actual solution than maximum likelihood (ML). In addition, it is found that the choice of the shape of the prior distribution affects the noise characteristics and edge sharpness in the final estimate.
Lalush et al. (Mon,) studied this question.