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Robust Maximum- Likelihood Position Estimation in Scintillation Cameras

dc.contributor.authorFessler, Jeffrey A.en_US
dc.contributor.authorRogers, W. Leslieen_US
dc.contributor.authorClinthorne, Neal H.en_US
dc.date.accessioned2011-08-18T18:20:59Z
dc.date.available2011-08-18T18:20:59Z
dc.date.issued1991-11-02en_US
dc.identifier.citationFessler, J.A.; Rogers, W.L.; Clinthorne, N.H. (1991). "Robust Maximum- Likelihood Position Estimation in Scintillation Cameras." Conference Record of the … IEEE Nuclear Science Symposium and Medical Imaging Conference 3: 1851-1855. <http://hdl.handle.net/2027.42/85879>en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/85879
dc.description.abstractThe classical maximum-likelihood (ML) estimator for the position of a scintillation event in a gamma camera, as derived by Gray and Macovski in 1976, requires exact knowledge of the light-spread function (LSF) of each photomultiplier tube. In practice, one must determine each LSF from noisy measurements corrupted by Poisson noise, quantization error, and electronic noise and bias. Since the ML position estimator involves derivatives of each LSF, even small measurement errors can result in degraded estimator performance. In this paper we derive a robust ML position estimator that accounts for the statistical uncertainty in LSF measurements. The form of the robust estimator diminishes contributions from the tails of the LSF, where the relative measurement errors are the largest.en_US
dc.publisherIEEEen_US
dc.titleRobust Maximum- Likelihood Position Estimation in Scintillation Camerasen_US
dc.typeArticleen_US
dc.subject.hlbsecondlevelBiomedical Engineeringen_US
dc.subject.hlbtoplevelEngineeringen_US
dc.description.peerreviewedPeer Revieweden_US
dc.contributor.affiliationumDivision of Nuclear Medicine.en_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/85879/1/Fessler117.pdf
dc.identifier.doi10.1109/NSSMIC.1991.259236en_US
dc.identifier.sourceConference Record of the … IEEE Nuclear Science Symposium and Medical Imaging Conferenceen_US
dc.owningcollnameElectrical Engineering and Computer Science, Department of (EECS)


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