Grouped-Coordinate Ascent Algorithms for Penalized-Likelihood Transmission Image Reconstruction
dc.contributor.author | Fessler, Jeffrey A. | en_US |
dc.contributor.author | Ficaro, E. P. | en_US |
dc.contributor.author | Clinthorne, N. H. | en_US |
dc.contributor.author | Lange, Kenneth | en_US |
dc.date.accessioned | 2011-08-18T18:21:23Z | |
dc.date.available | 2011-08-18T18:21:23Z | |
dc.date.issued | 1997-04 | en_US |
dc.identifier.citation | Fessler, J.A.; Ficaro, E.P.; Clinthorne, N.H.; Lange, K. (1997). "Grouped-Coordinate Ascent Algorithms for Penalized-Likelihood Transmission Image Reconstruction". IEEE Transactions on Medical Imaging 16(2): 166-175. <http://hdl.handle.net/2027.42/86021> | en_US |
dc.identifier.issn | 0278-0062 | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/86021 | |
dc.description.abstract | Presents a new class of algorithms for penalized-likelihood reconstruction of attenuation maps from low-count transmission scans. We derive the algorithms by applying to the transmission log-likelihood a version of the convexity technique developed by De Pierro for emission tomography. The new class includes the single-coordinate ascent (SCA) algorithm and Lange's convex algorithm for transmission tomography as special cases. The new grouped-coordinate ascent (GCA) algorithms in the class overcome several limitations associated with previous algorithms. (1) Fewer exponentiations are required than in the transmission maximum likelihood-expectation maximization (ML-EM) algorithm or in the SCA algorithm. (2) The algorithms intrinsically accommodate nonnegativity constraints, unlike many gradient-based methods. (3) The algorithms are easily parallelizable, unlike the SCA algorithm and perhaps line-search algorithms. We show that the GCA algorithms converge faster than the SCA algorithm, even on conventional workstations. An example from a low-count positron emission tomography (PET) transmission scan illustrates the method. | en_US |
dc.publisher | IEEE | en_US |
dc.title | Grouped-Coordinate Ascent Algorithms for Penalized-Likelihood Transmission Image Reconstruction | 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 | University of Michigan | en_US |
dc.identifier.pmid | 9101326 | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/86021/1/Fessler93.pdf | |
dc.identifier.doi | 10.1109/42.563662 | en_US |
dc.identifier.source | IEEE Transactions on Medical Imaging | en_US |
dc.owningcollname | Electrical Engineering and Computer Science, Department of (EECS) |
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