Image Reconstruction Using A Priori Boundary Information

We describe a Bayesian model for the reconstruction of images based on projection data. The model incorporates a boundary process to sever correlations between neighboring regions within images, and the prior distribution of the boundary process can be modified easily in situations in which precise boundary information is available a priori. An example of a positron emission tomography image reconstructed using boundaries obtained from a high resolution magnetic resonance image is provided.