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Joint Estimation of Respiratory Motion and Activity in 4D PET Using CT Side Information

dc.contributor.authorJacobson, Matthew W.en_US
dc.contributor.authorFessler, Jeffrey A.en_US
dc.date.accessioned2011-08-18T18:20:46Z
dc.date.available2011-08-18T18:20:46Z
dc.date.issued2006-04-06en_US
dc.identifier.citationJacobson, M.W.; Fessier, J.A. (2006). "Joint Estimation of Respiratory Motion and Activity in 4D PET Using CT Side Information."IEEE International Symposium on Biomedical Imaging: Nano to Macro: 275-278. <http://hdl.handle.net/2027.42/85812>en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/85812
dc.description.abstractIn previous work, we proposed a Poisson statistical model for gated PET data in which the distribution was parametrized in terms of both image intensity and motion parameters. The motion parameters related the activity image in each gate to that of a base image in some fixed gate. By doing maximum loglikelihood (ML) estimation of all parameters simultaneously, one obtains an estimate of the base gate image that exploits the full set of measured sinogram data. Previously, this joint ML approach was compared, in a highly simplified single-slice setting, to more conventional methods. Performance was measured in terms of the recovery of tracer uptake in a synthetic lung nodule. This paper reports the extension to 3D with much more realistic simulated motion. Furthermore, in addition to pure ML estimation, we consider the use of side information from a breath-hold CT scan to facilitate regularization, while preserving hot lesions of the kind seen in FDG oncology studies.en_US
dc.publisherIEEEen_US
dc.titleJoint Estimation of Respiratory Motion and Activity in 4D PET Using CT Side Informationen_US
dc.typeArticleen_US
dc.subject.hlbsecondlevelBiomedical Engineeringen_US
dc.subject.hlbtoplevelEngineeringen_US
dc.description.peerreviewedPeer Revieweden_US
dc.contributor.affiliationumDepartment of Electrical Engineering and Computer Scienceen_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/85812/1/Fessler219.pdf
dc.identifier.doi10.1109/ISBI.2006.1624906en_US
dc.identifier.sourceIEEE International Symposium on Biomedical Imaging: Nano to Macroen_US
dc.owningcollnameElectrical Engineering and Computer Science, Department of (EECS)


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