Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1899
Title: Enhanced formation of parametric images using fast regressive GLLS for noisy functional imaging
Authors: Wen, L
Eberl, S
Bai, J
Feng, DD
Keywords: Monte Carlo methods
Least squares approximations
Medical image processing
Regression analysis
Single photon emission computed tomography
Issue Date: 2007
Publisher: IEEE
Source: EMBS 2007 : 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society : proceedings, Lyon, France, 23-26 August 2007, p. 4177-4180 How to cite?
Abstract: Parametric images derived in functional imaging can visualize the spatial distribution of physiological parameters in vivo. However, the high level of noise intrinsic in single photon emission computed tomography (SPECT) may lead to physiologically meaningless parameter estimates such as negative kinetic rate constants using the generalized linear least squares (GLLS) method for compartmental model fitting. In this study, an enhanced GLLS method using fast regressive adjustment of parameters was investigated for improving the reliability of GLLS applied to dynamic SPECT data. Monte Carlo simulation data were used to systematically evaluate accuracy and reliability of derived parametric images. The simulation and experimental results demonstrate that the enhanced GLLS method can achieve more reliable parametric images, while largely preserving computational efficiency.
URI: http://hdl.handle.net/10397/1899
ISBN: 1-4244-0788-5
DOI: 10.1109/IEMBS.2007.4353257
Rights: © 2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
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