Ordered subsets algorithms for transmission tomography
dc.contributor.author | Erdogan, Hakan | en_US |
dc.contributor.author | Fessler, Jeffrey A. | en_US |
dc.date.accessioned | 2006-12-19T19:03:09Z | |
dc.date.available | 2006-12-19T19:03:09Z | |
dc.date.issued | 1999-11-01 | en_US |
dc.identifier.citation | Erdogan, H; Fessler, J A (1999). "Ordered subsets algorithms for transmission tomography." Physics in Medicine and Biology. 44(11): 2835-2851. <http://hdl.handle.net/2027.42/48964> | en_US |
dc.identifier.issn | 0031-9155 | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/48964 | |
dc.identifier.uri | http://www.ncbi.nlm.nih.gov/sites/entrez?cmd=retrieve&db=pubmed&list_uids=10588288&dopt=citation | en_US |
dc.description.abstract | The ordered subsets EM (OSEM) algorithm has enjoyed considerable interest for emission image reconstruction due to its acceleration of the original EM algorithm and ease of programming. The transmission EM reconstruction algorithm converges very slowly and is not used in practice. In this paper, we introduce a simultaneous update algorithm called separable paraboloidal surrogates (SPS) that converges much faster than the transmission EM algorithm. Furthermore, unlike the `convex algorithm' for transmission tomography, the proposed algorithm is monotonic even with nonzero background counts. We demonstrate that the ordered subsets principle can also be applied to the new SPS algorithm for transmission tomography to accelerate `convergence', albeit with similar sacrifice of global convergence properties as for OSEM. We implemented and evaluated this ordered subsets transmission (OSTR) algorithm. The results indicate that the OSTR algorithm speeds up the increase in the objective function by roughly the number of subsets in the early iterates when compared to the ordinary SPS algorithm. We compute mean square errors and segmentation errors for different methods and show that OSTR is superior to OSEM applied to the logarithm of the transmission data. However, penalized-likelihood reconstructions yield the best quality images among all other methods tested. | en_US |
dc.format.extent | 3118 bytes | |
dc.format.extent | 269467 bytes | |
dc.format.mimetype | text/plain | |
dc.format.mimetype | application/pdf | |
dc.language.iso | en_US | |
dc.publisher | IOP Publishing Ltd | en_US |
dc.title | Ordered subsets algorithms for transmission tomography | en_US |
dc.type | Article | en_US |
dc.subject.hlbsecondlevel | Physics | en_US |
dc.subject.hlbtoplevel | Science | en_US |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.contributor.affiliationum | 4415 EECS Building, 1301 Beal Avenue, University of Michigan, Ann Arbor, MI 48109-2122, USA | en_US |
dc.contributor.affiliationum | 4415 EECS Building, 1301 Beal Avenue, University of Michigan, Ann Arbor, MI 48109-2122, USA | en_US |
dc.identifier.pmid | 10588288 | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/48964/2/m91111.pdf | en_US |
dc.identifier.doi | http://dx.doi.org/10.1088/0031-9155/44/11/311 | en_US |
dc.identifier.source | Physics in Medicine and Biology. | en_US |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
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