Please use this identifier to cite or link to this item: https://open.uns.ac.rs/handle/123456789/13382
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dc.contributor.authorBezbradica M.en
dc.contributor.authorTrpovski, Željenen
dc.date.accessioned2020-03-03T14:52:06Z-
dc.date.available2020-03-03T14:52:06Z-
dc.date.issued2008-12-01en
dc.identifier.isbn9781424429042en
dc.identifier.urihttps://open.uns.ac.rs/handle/123456789/13382-
dc.description.abstractPositron Emission Tomography (PET) is used extensively in image acquisition for medical purposes. It is necessary to perform an evaluation of parameters that are required for image reconstruction based on the measured data. The most commonly used method for estimation of PET parameters is Expectation Maximization algorithm. About a decade ago a new SAGE algorithm was developed and soon It began to be used in image reconstruction. Principles and examples of these algorithms as well as of PET are described in this paper. ©2008 IEEE.en
dc.relation.ispartof9th Symposium on Neural Network Applications in Electrical Engineering, NEUREL 2008 Proceedingsen
dc.titlePrinciples of image reconstruction using positron emission tomography and maximum likelihood estimation algorithmsen
dc.typeConference Paperen
dc.identifier.doi10.1109/NEUREL.2008.4685592en
dc.identifier.scopus2-s2.0-58049156433en
dc.identifier.urlhttps://api.elsevier.com/content/abstract/scopus_id/58049156433en
dc.relation.lastpage144en
dc.relation.firstpage141en
item.grantfulltextnone-
item.fulltextNo Fulltext-
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