'Institute of Electrical and Electronics Engineers (IEEE)'
Doi
Abstract
This paper reviews and compares three maximum
likelihood algorithms for transmission tomography. One of these
algorithms is the EM algorithm, one is based on a convexity
argument devised by De Pierro in the context of emission tomography,
and one is an ad hoc gradient algorithm. The algorithms
enjoy desirable local and global convergence properties and
combine gracefully with Bayesian smoothing priors. Preliminary
numerical testing of the algorithms on simulated data suggest
that the convex algorithm and the ad hoc gradient algorithm are
computationally superior to the EM algorithm. This superiority
stems from the larger number of exponentiations required by
the EM algorithm. The convex and gradient algorithms are well
adapted to parallel computing.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/86016/1/Fessler101.pd