Bayesian estimation from projections with low photon dosages

A method is presented for Bayesian reconstruction from projections which updates single pixel values, rather than the entire image, at each step. The technique is similar to Gauss-Seidel (GS) iteration for the solution of differential equations on finite grids. The computational cost per iteration of the GS approach is found to be approximately equal to that of gradient methods. For continuously valued images, GS is found to have significantly better convergence at modes representing high spatial frequencies. In addition, GS is well-suited to segmentation when the image is constrained to be discrete-valued