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Low-Dose Dual-Energy Computed Tomography for PET Attenuation Correction with Statistical Sinogram Restoration

dc.contributor.authorNoh, Joonkien_US
dc.contributor.authorFessler, Jeffrey A.en_US
dc.contributor.authorKinahan, Paul E.en_US
dc.date.accessioned2011-08-18T18:21:09Z
dc.date.available2011-08-18T18:21:09Z
dc.date.issued2008-02-18en_US
dc.identifier.citationNoh, J.; Fessler, J. A.; Kinahan, P. E. (2008). "Low-Dose Dual-Energy Computed Tomography for PET Attenuation Correction with Statistical Sinogram Restoration ." Proc. Of SPIE. Medical Imaging: Image Processing 6913: 691312:1-10. <http://hdl.handle.net/2027.42/85933>en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/85933
dc.description.abstractDual-energy (DE) X-ray computed tomography (CT) has been proposed as an useful tool in various applications. One promising application is DECT with low radiation doses used for attenuation correction in positron emission tomography (PET). In low-dose DECT, conventional methods for sinogram decomposition have been based on logarithmic transformations and ignored noise properties, leading to very noisy component sinogram estimates. In this paper, we propose two novel sinogram restoration methods that are statistically motivated; penalized weighted least square (PWLS) and penalized likelihood (PL), producing less noisy component sinogram estimates for low-dose DECT than the conventional approaches. The restored component sinograms can improve attenuation correction, thus allowing better image quality in PET. Experiments with a digital phantom indicate that the proposed methods produce less noisy sinograms, reconstructed images, and attenuation correction factors (ACF) than the conventional one, showing promise for CT-based attenuation correction in emission tomography.en_US
dc.publisherSPIEen_US
dc.titleLow-Dose Dual-Energy Computed Tomography for PET Attenuation Correction with Statistical Sinogram Restorationen_US
dc.typeArticleen_US
dc.subject.hlbsecondlevelBiomedical Engineeringen_US
dc.subject.hlbtoplevelEngineeringen_US
dc.description.peerreviewedPeer Revieweden_US
dc.contributor.affiliationumDepartment of Electrical Engin. and Computer Science.en_US
dc.contributor.affiliationotherDepartment of Radiology, Imaging Research Lab., Univ. of Washington, Seattle, WA, USA.en_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/85933/1/Fessler230.pdf
dc.identifier.doi10.1117/12.769855en_US
dc.identifier.sourceProc. Of SPIE. Medical Imaging: Image Processingen_US
dc.owningcollnameElectrical Engineering and Computer Science, Department of (EECS)


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