Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/10026
Title: Temporal compression for dynamic positron emission tomography via principal component analysis in the sinogram domain
Authors: Chen, Z
Parker, B
Feng, D
Keywords: Brain
Data compression
Image coding
Image reconstruction
Medical computing
Noise
Phantoms
Positron emission tomography
Principal component analysis
Issue Date: 2003
Publisher: IEEE
Source: 2003 IEEE Nuclear Science Symposium Conference Record, 19-25 October 2003, v. 4, p. 2858-2862 How to cite?
Abstract: We compare dynamic PET temporal compression using optimal sampling schedule design, principal component analysis (PCA) in the image domain, and principal component analysis in the sinogram domain. For region-of-interest quantification, sinogram-domain PCA is combined with the Huesman algorithm to quantify from the sinograms directly without requiring full frame reconstruction. Using a simulated phantom FDG brain study and three clinical studies, we evaluate the fidelity of the compressed data for estimation of local cerebral metabolic rate of glucose by a four-compartment model. Our results show that using a (noise-normalized) PCA in the sinogram-domain gives similar compression ratio and quantitative accuracy to OSS, but with much better precision. These results indicate that sinogram-domain PCA for dynamic PET can be a useful preprocessing stage for PET compression and estimation applications.
URI: http://hdl.handle.net/10397/10026
ISBN: 0-7803-8257-9
ISSN: 1082-3654
DOI: 10.1109/NSSMIC.2003.1352480
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