Tomographic image sequence reconstruction by edge-preserving interslice MAP methods

We consider a new problem in tomographic imaging. In a sequence of transverse images, adjacent slices often look similar to each other. We investigate the possibility of using this correlation to help reconstruct the sequence with 2-D in-plane data. The problem is formulated as a maximum a posteriori (MAP) estimation problem, using an edge-preserving Markov random field prior model. Both Gaussian and Poisson data are considered. Some improvements over the single-slice MAP reconstructions are observed.