Hidden-Data Spaces for Maximum-Likelihood PET Reconstruction

Fessler, Jeffrey A.

Hidden-Data Spaces for Maximum-Likelihood PET Reconstruction

dc.contributor.author	Fessler, Jeffrey A.	en_US
dc.date.accessioned	2011-08-18T18:21:23Z
dc.date.available	2011-08-18T18:21:23Z
dc.date.issued	1992-10-25	en_US
dc.identifier.citation	Fessler, A. (1992). "Hidden-Data Spaces for Maximum-Likelihood PET Reconstruction." Conference Record of the … IEEE Nuclear Science Symposium and Medical Imaging Conference 2: 898-900. <http://hdl.handle.net/2027.42/86022>	en_US
dc.identifier.uri	https://hdl.handle.net/2027.42/86022
dc.description.abstract	The author shows that expectation-maximization (EM) algorithms based on smaller complete data spaces will typically converge faster. As an example, he compares the two maximum-likelihood (ML) image reconstruction algorithms of D. G. Politte and D. L. Snyder (1991) which are based on measurement models that account for attenuation and accidental coincidences in positron-emission tomography (PET).	en_US
dc.publisher	IEEE	en_US
dc.title	Hidden-Data Spaces for Maximum-Likelihood PET 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.description.bitstreamurl	http://deepblue.lib.umich.edu/bitstream/2027.42/86022/1/Fessler120.pdf
dc.identifier.doi	10.1109/NSSMIC.1992.301014	en_US
dc.identifier.source	Conference Record of the … IEEE Nuclear Science Symposium and Medical Imaging Conference	en_US
dc.owningcollname	Electrical Engineering and Computer Science, Department of (EECS)

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