Bayesian multiscale tomographic reconstruction

This paper describes a new Bayesian modeling and analysis method for emission computed tomography based on a novel multiscale framework. The class of multiscale priors has the interesting feature that the “non-informative” member yields the traditional maximum likelihood solution; other choices are made to reflect prior belief as to the smoothness of the unknown intensity. Remarkably, this Bayesian multiscale framework admits a novel maximum a posteriori (MAP) reconstruction procedure using an expectation-maximization (EM) algorithm, in which the EM update equations have simple, closed-form expressions. The potential of this new framework is assessed using the Zubal brain phantom and simulated SPECT studies