Accelerated deterministic annealing algorithms for transmission tomography using ordered subsets of projection data

An approach to the maximum a posterior estimation of attenuation coefficients in transmission tomography is presented. The prior distribution used in our algorithm is based on the line-process model, which has an ability to signal the presence of discontinuities in reconstructed images. This model is particularly applicable to transmission tomography for chest slices, where the anatomical regions are significantly different in their attenuation. To optimize our nonconvex objective function, we use our previously developed deterministic annealing (DA) algorithm and accelerate its convergence by applying the ordered subsets (OS) principle to an annealing schedule. Our simulation results show that, as the number of subsets increases, the OS procedure applied to our DA algorithm accelerates convergence by a factor proportional to the number of subsets in the early iterates when compared to the standard DA algorithm. The net conclusion is that the OS method makes the DA algorithm more useful for reconstructing attenuation maps with anatomical borders by providing a substantial acceleration