TY - GEN
T1 - Reducing the noise effects in Logan graphic analysis for PET receptor measurements
AU - Guo, Hongbin
AU - Chen, Kewei
AU - Renaut, Rosemary
AU - Reiman, Eric M.
N1 - Copyright:
Copyright 2010 Elsevier B.V., All rights reserved.
PY - 2009
Y1 - 2009
N2 - Logan's graphical analysis (LGA) is a widely-used approach for quantification of biochemical and physiological processes from Positron emission tomography (PET) image data. A well-noted problem associated with the LGA method is the bias in the estimated parameters. We recently systematically evaluated the bias associated with the linear model approximation and developed an alternative to minimize the bias due to model error. In this study, we examined the noise structure in the equations defining linear quantification methods, including LGA. The noise structure conflicts with the conditions given by the Gauss-Markov theorem for the least squares (LS) solution to generate the best linear unbiased estimator. By carefully taking care of the data error structure, we propose to use structured total least squares (STLS) to obtain the solution using a one-dimensional optimization problem. Simulations of PET data for [11C] benzothiazole-aniline (Pittsburgh Compound-B [PIB]) show that the proposed method significantly reduces the bias. We conclude that the bias associated with noise is primarily due to the unusual structure of he correlated noise and it can be reduced with the proposed STLS method.
AB - Logan's graphical analysis (LGA) is a widely-used approach for quantification of biochemical and physiological processes from Positron emission tomography (PET) image data. A well-noted problem associated with the LGA method is the bias in the estimated parameters. We recently systematically evaluated the bias associated with the linear model approximation and developed an alternative to minimize the bias due to model error. In this study, we examined the noise structure in the equations defining linear quantification methods, including LGA. The noise structure conflicts with the conditions given by the Gauss-Markov theorem for the least squares (LS) solution to generate the best linear unbiased estimator. By carefully taking care of the data error structure, we propose to use structured total least squares (STLS) to obtain the solution using a one-dimensional optimization problem. Simulations of PET data for [11C] benzothiazole-aniline (Pittsburgh Compound-B [PIB]) show that the proposed method significantly reduces the bias. We conclude that the bias associated with noise is primarily due to the unusual structure of he correlated noise and it can be reduced with the proposed STLS method.
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U2 - 10.1109/ICCME.2009.4906641
DO - 10.1109/ICCME.2009.4906641
M3 - Conference contribution
AN - SCOPUS:67650676033
SN - 9781424433162
T3 - 2009 ICME International Conference on Complex Medical Engineering, CME 2009
BT - 2009 ICME International Conference on Complex Medical Engineering, CME 2009
T2 - 2009 ICME International Conference on Complex Medical Engineering, CME 2009
Y2 - 9 April 2009 through 11 April 2009
ER -