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Quadratic Regularization Design for 3d Cylindrical PET

dc.contributor.authorShi, Hugoen_US
dc.contributor.authorFessler, Jeffrey A.en_US
dc.date.accessioned2011-08-18T18:20:55Z
dc.date.available2011-08-18T18:20:55Z
dc.date.issued2005-10-23en_US
dc.identifier.citationShi, H.; Fessier, J.A. (2005). "Quadratic Regularization Design for 3d Cylindrical PET." IEEE Nuclear Science Symposium Conference Record: 2301-2305. <http://hdl.handle.net/2027.42/85859>en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/85859
dc.description.abstractStatistical methods for tomographic image reconstruction lead to improved spatial resolution and noise properties in PET. Penalized-likelihood (PL) image reconstruction methods involve maximizing an objective function that is based on the log-likelihood of the sinogram measurements and on a roughness penalty function to control noise. In emission tomography, PL methods (and MAP methods) based on conventional quadratic regularization functions lead to nonuniform and anisotropic spatial resolution, even for idealized shift-invariant imaging systems. We have previously addressed this problem for parallel-beam 2D emission tomography, and for fan-beam 2D transmission tomography by designing data-dependent, shift-variant regularizers that improve resolution uniformity and isotropy, even for idealized shift-invariant imaging systems. This paper extends those methods to 3D cylindrical PET, using an analytical design approach that is numerically efficient.en_US
dc.publisherIEEEen_US
dc.titleQuadratic Regularization Design for 3d Cylindrical PETen_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/2027.42/85859/1/Fessler212.pdf
dc.identifier.doi10.1109/NSSMIC.2005.1596794en_US
dc.identifier.sourceIEEE Nuclear Science Symposium Conference Recorden_US
dc.owningcollnameElectrical Engineering and Computer Science, Department of (EECS)


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