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ABSTRACT: Tracer kinetic modeling with positron emission tomography (PET) requires measurements of the time-activity curves in both plasma (PTAC) and tissue (TTAC) to estimate physiological parameters, i.e. to fit the parameters of certain compartmental models using PTAC and TTAC as the model input and output functions, respectively. In this paper, we first explored the optimal blood sampling schedule (OBSS) for the input function, based on the tracer [18F]2-fluoro-2-deoxy-D-glucose (FDG) blood sample experimental data. Then using a 5-parameter FDG model we investigated the effects of the plasma sampling schedule, as well as PTAC measurement noise, on the estimation accuracy and reliability of FDG model macro- and micro-parameters and the physiological parameter local cerebral metabolic rates of glucose (LCMRGlc), using computer simulation. Three different methods were used: (a) estimation of the FDG model parameters ignoring PTAC noise using the traditional PTAC schedule (non-OBSS); (b) estimation of the PTAC model parameters and FDG model parameters simultaneously using both non-OBSS and OBSS; (c) estimation of the PTAC model parameters first, then the FDG model parameters using both non-OBSS and OBSS. The results show that OBSS can provide more reliable estimates and largely simplifies the experiment operations.
Computer Methods and Programs in Biomedicine 12/1994; 45(3):175-86. · 1.52 Impact Factor
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ABSTRACT: In tracer kinetic modeling with Positron Emission Tomography (PET), the direct measurement (piecewise linear approximation) of plasma time-activity curve of tracer (PTAC) is often used as the input function to estimate regional physiological parameters. However, no explicit general model is available for PTAC itself, which limits the further study of the effects of PTAC, such as PTAC measurement noise or PTAC sampling schedules, on the physiological parameters estimation. A PTAC model is proposed in this paper and compared with other four possible candidates. Eight sets of [18F]-fluoro-2-deoxy-D-glucose (FDG) experimental data were used to test the models and several statistical criteria were used to validate their adequacy. An application of the model to improve the estimation of local cerebral metabolic rate of glucose (LCMRGlc) is presented. This model is also expected to be useful for generating realistic PTAC curves in computer simulation studies of other tracers and their kinetic modeling characteristics.
International Journal of Bio-Medical Computing 04/1993; 32(2):95-110.
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ABSTRACT: Tracer kinetic modeling with positron emission tomography (PET) requires measurements of the time-activity curves in both plasma (PTAC) and tissue (TTAC) to estimate physiological parameters, i.e. to fit the parameters of certain compartmental models using PTAC and TTAC as the model input and output functions, respectively. However, the estimation usually ignores the measurement noise in plasma tracer activity curves. The accuracy and reliability of the physiological parameters estimated by ignoring such noise are not well understood. In this paper, effects of noise in [18F] 2-fluoro-2-deoxy-D-glucose (FDG) tracer plasma concentration measurements on estimation of local cerebral metabolic rates of glucose (LCMRGlc) with PET is investigated systematically. The PTAC modeling approach used in this paper also provides a realistic means to filter out the noise and to improve the physiological parameter accuracy, which can be potentially used in model-based non-invasive measurements of PTAC.
Computers in Biology and Medicine 02/1993; 23(1):57-68. · 1.09 Impact Factor
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ABSTRACT: Tracer kinetic modeling with Positron Emission Tomography (PET) requires measurements of the time-activity curves in both plasma (PTAC) and tissue (TTAC) to estimate physiological parameters. However, the estimation usually ignores the measurement noise in plasma tracer activity curves. The accuracy and reliability of the physiological parameters estimated by ignoring such noise are not well understood. In this paper, computer simulations were performed to investigate the influence of input measurement noise on the accuracy of estimates. The results show that input measurement noise causes considerable variability in the parameter estimates.
Biomedical sciences instrumentation 02/1991; 27:43-8.
