NDLI: An adaptation of ridge regression for improved estimation of kinetic model parameters from PET studies

Content Provider	IEEE Xplore Digital Library
Author	Byrtek, M. O'Sullivan, F. Muzi, M. Spence, A.M.
Copyright Year	2004
Description	Author affiliation: Dept. of Stat., Univ. Coll. Cork, Ireland (Byrtek, M.; O'Sullivan, F.)
Abstract	The quantitative analysis of dynamic PET data to obtain kinetic constants in compartmental models involves the use of non-linear weighted least squares regression. Current estimation techniques often have poor mean square error estimation properties. Ridge regression is a technique for improving parameter estimation accuracy in ordinary linear regression. This technique has been found to have potential for improving mean square error when adapted to the nonlinear PET estimation problem. The effectiveness of ridge regression in this context, however, relies heavily on the correct selection of an unknown biasing parameter and the precise specification of a penalty function. In this study, an approach is explored for improving the effectiveness of ridge regression by incorporation of more rigorous Bayesian formulations for specification of the ridge penalty function. Using a variance component model, a prior covariance for the ridge penalty term is developed. Application of the resulting ridge estimation technique shows that the use of the Bayesian formulation for the penalty can reduce current ridge regression parameter loss by up to 19%. An adaptive approach to the selection of the biasing parameter is also developed.
Starting Page	3120
Ending Page	3124
File Size	414445
Page Count	5
File Format	PDF
ISBN	0780382579
ISSN	10823654
DOI	10.1109/NSSMIC.2003.1352558
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-10-19
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Kinetic theory Positron emission tomography Parameter estimation Mean square error methods Bayesian methods Least squares approximation Cancer Statistics Nuclear and plasma sciences Context modeling
Content Type	Text
Resource Type	Article

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