Low-Dose Dual-Energy Computed Tomography for PET Attenuation Correction with Statistical Sinogram Restoration
dc.contributor.author | Noh, Joonki | en_US |
dc.contributor.author | Fessler, Jeffrey A. | en_US |
dc.contributor.author | Kinahan, Paul E. | en_US |
dc.date.accessioned | 2011-08-18T18:21:09Z | |
dc.date.available | 2011-08-18T18:21:09Z | |
dc.date.issued | 2008-02-18 | en_US |
dc.identifier.citation | Noh, 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.uri | https://hdl.handle.net/2027.42/85933 | |
dc.description.abstract | Dual-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.publisher | SPIE | en_US |
dc.title | Low-Dose Dual-Energy Computed Tomography for PET Attenuation Correction with Statistical Sinogram Restoration | 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 Engin. and Computer Science. | en_US |
dc.contributor.affiliationother | Department of Radiology, Imaging Research Lab., Univ. of Washington, Seattle, WA, USA. | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/85933/1/Fessler230.pdf | |
dc.identifier.doi | 10.1117/12.769855 | en_US |
dc.identifier.source | Proc. Of SPIE. Medical Imaging: Image Processing | en_US |
dc.owningcollname | Electrical Engineering and Computer Science, Department of (EECS) |
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