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Scan Time Optimization for Post-injection PET Scans

dc.contributor.authorErdogan, Hakanen_US
dc.contributor.authorFessler, Jeffrey A.en_US
dc.date.accessioned2011-08-18T18:20:59Z
dc.date.available2011-08-18T18:20:59Z
dc.date.issued1998-11-08en_US
dc.identifier.citationErdogan, H.; Fessler, J.A. (1998). "Scan Time Optimization for Post-injection PET Scans." IEEE Conference Record of Nuclear Science Symposium 3: 1842-1846. <http://hdl.handle.net/2027.42/85880>en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/85880
dc.description.abstractPrevious methods for optimizing the scan times for PET transmission and emission scans under a total scan time constraint were based on linear non-statistical methods and used noise equivalent counts (NEC) criteria. The scan times determined by NEC analysis may be suboptimal when nonlinear statistical image reconstruction methods are used. For statistical image reconstruction, the predicted variance in selected regions of interest is an appropriate alternative to NEC analysis. The authors propose a new method for optimizing the relative scan times (fractions) based on analytical approximations to the covariance of images reconstructed by both conventional and penalized-likelihood methods. The authors perform simulations to compare predicted standard deviations with empirical ones. Results show that for statistical transmission image reconstruction, the optimal fraction of the scan time devoted to transmission scanning is shorter than for conventional transmission smoothing.en_US
dc.publisherIEEEen_US
dc.titleScan Time Optimization for Post-injection PET Scansen_US
dc.typeArticleen_US
dc.subject.hlbsecondlevelBiomedical Engineeringen_US
dc.subject.hlbtoplevelEngineeringen_US
dc.description.peerreviewedPeer Revieweden_US
dc.contributor.affiliationumDept. of EECS.en_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/85880/1/Fessler154.pdf
dc.identifier.doi10.1109/NSSMIC.1998.773894en_US
dc.identifier.sourceIEEE Conference Record of Nuclear Science Symposiumen_US
dc.owningcollnameElectrical Engineering and Computer Science, Department of (EECS)


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