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Consequences of Using a Simplified Kinetic Model for Dynamic PET Data

Center for Functional Imaging, Lawrence Berkeley National Laboratorv and Department of Physics, University of California, Berkeley, California

Correspondence: For correspondence or reprints contact: Pamela G. Coxson, MS 55-121 Lawrence Berkeley National Laboratory, 1 Cyclotron Rd., Berkeley, CA 94720.

ABSTRACT

We compared a physiological model of ⁸²Rb kinetics in the myocardium with two reduced-order models to determine their usefulness in assessing physiological parameters from dynamic PET data. Methods: A three-compartment model of ⁸²Rb in the myocardium was used to simulate kinetic PET ROI data. Simulations were generated for eight different blood-flow rates reflecting the physiological range of interest. Two reduced-order models commonly used with myocardial PET studies were fit to the simulated data, and parameters of the reduced-order models were compared with the physiological parameters. Then all three models were fit to the simulated data with noise added. Monte Carlo simulations were used to evaluate and compare the diagnostic utility of the reduced-order models. A description length criterion was used to assess goodness of fit for each model. Finally, fits to simulated data were compared with fits to actual dynamic PET data. Results: Fits of the reduced-order models to the three-compartment model noise-free simulated data produced model misspecification artifacts, such as flow parameter bias and systematic variation with flow in estimates of nonflow parameters. Monte Carlo simulations showed some of the parameter estimates for the two-compartment model to be highly variable at PET noise levels. Fits to actual PET data showed similar variability. One-compartment model estimates of the flow parameter at high and low flow were separated by several s.d.s for both the simulated and the real data. With the two-compartment model, the separation was about one s.d., making it difficult to differentiate a high and a low flow in a single experiment. Fixing nonflow parameters reduced flow parameter variability in the two-compartment model and did not significantly affect variability in the one-compartment model. Goodness of fit indicated that, at realistic noise levels, both reduced-order models fit the simulated data at least as well as the three-compartment model that generated the data. Conclusion: The one-compartment reduced-order model of ⁸²Rb dynamic PET data can be used effectively to compare myocardial blood-flow rates at rest and stress levels. The two-compartment model can differentiate flow only if a priori values are used for nonflow parameters.

Key Words: PET • physiological models • rubidium-82 • myocardial blood flow

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