Globally Convergent Algorithms for Maximum a Posteriori Transmission Tomography
dc.contributor.author | Lange, Kenneth | en_US |
dc.contributor.author | Fessler, Jeffrey A. | en_US |
dc.date.accessioned | 2011-08-18T18:21:22Z | |
dc.date.available | 2011-08-18T18:21:22Z | |
dc.date.issued | 1995-10 | en_US |
dc.identifier.citation | Lange, Kenneth; Fessler, Jeffrey A. (1995). "Globally Convergent Algorithms for Maximum a Posteriori Transmission Tomography." IEEE Transactions on Image Processing 4(10): 1430-1438. <http://hdl.handle.net/2027.42/86016> | en_US |
dc.identifier.issn | 1057-7149 | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/86016 | |
dc.description.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. | en_US |
dc.publisher | IEEE | en_US |
dc.title | Globally Convergent Algorithms for Maximum a Posteriori Transmission Tomography | en_US |
dc.type | Article | en_US |
dc.subject.hlbsecondlevel | Biomedical Engineering | en_US |
dc.subject.hlbtoplevel | Engineering | en_US |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.contributor.affiliationum | Department of Biostatistics. | en_US |
dc.identifier.pmid | 18291974 | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/86016/1/Fessler101.pdf | |
dc.identifier.doi | 10.1109/83.465107 | en_US |
dc.identifier.source | IEEE Transactions on Image Processing | en_US |
dc.owningcollname | Electrical Engineering and Computer Science, Department of (EECS) |
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