We quantitatively and qualitatively examine the use of a Gibbs prior in maximum a posteriori (MAP) reconstruction of SPECT images of pulmonary perfusion using the expectationmaximization algorithm (EM). This Bayesian approach is applied to SPECT projection data acquired from a realistic torso phantom with spherical defects in the lungs simulating perfusion deficits. Both the scatter subtraction constant (k) and the smoothing parameier beta (p) characterizing the prior are varied to study their affect on image quality and quantification. Region of interest (ROI) analysis is used to compare MAP-

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