Statistical Sinogram Restoration in Dual-Energy CT for PET Attenuation Correction
dc.contributor.author | Noh, Joonki | en_US |
dc.contributor.author | Fessler, Jeffrey A. | en_US |
dc.contributor.author | Kinahan, Paul E. | en_US |
dc.contributor.author | en_US | |
dc.date.accessioned | 2011-08-18T18:21:02Z | |
dc.date.available | 2011-08-18T18:21:02Z | |
dc.date.issued | 2009-03-23 | en_US |
dc.identifier.citation | Noh, J.; Fessler, J.A.; Kinahan, P.E. (2009). "Statistical Sinogram Restoration in Dual-Energy CT for PET Attenuation Correction." IEEE Transactions on Medical Imaging 28(11): 1688-1702. <http://hdl.handle.net/2027.42/85900> | en_US |
dc.identifier.issn | 0278-0062 | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/85900 | |
dc.description.abstract | Dual-energy (DE) X-ray computed tomography (CT) has been found useful in various applications. In medical imaging, one promising application is using low-dose DECT for attenuation correction in positron emission tomography (PET). Existing approaches to sinogram material decomposition ignore noise characteristics and are based on logarithmic transforms, producing noisy component sinogram estimates for low-dose DECT. In this paper, we propose two novel sinogram restoration methods based on statistical models: penalized weighted least square (PWLS) and penalized likelihood (PL), yielding less noisy component sinogram estimates for low-dose DECT than classical methods. The proposed methods consequently provide more precise attenuation correction of the PET emission images than do previous methods for sinogram material decomposition with DECT. We report simulations that compare the proposed techniques and existing approaches. | en_US |
dc.publisher | IEEE | en_US |
dc.title | Statistical Sinogram Restoration in Dual-Energy CT for PET Attenuation Correction | en_US |
dc.type | Article | en_US |
dc.subject.hlbsecondlevel | Biomedical Engineering | en_US |
dc.subject.hlbtoplevel | Engineering | en_US |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.contributor.affiliationum | Department of Electrical Engineering and Computer Science. Department of Radiology. | en_US |
dc.identifier.pmid | 19336292 | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/85900/1/Fessler11.pdf | |
dc.identifier.doi | 10.1109/TMI.2009.2018283 | en_US |
dc.identifier.source | IEEE Transactions on Medical Imaging | en_US |
dc.owningcollname | Electrical Engineering and Computer Science, Department of (EECS) |
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