Computation of variance in compartment model parameter estimates from dynamic PET data

Medical Physics
Mustafa E Kamasak

Abstract

This paper investigates the validity of the analytical framework for variance ("analytical variance") in kinetic parameter and macroparameter estimations. Analytical variance is compared against the variance obtained from Monte Carlo simulations ("MC variance") for two different compartment models at different noise levels. Kinetic parameters for one-tissue (1T) and two-tissue (2T) compartment models are used to generate time-activity curves (TAC). Gaussian noise is added to the noiseless TAC to generate noise realizations for each noise level. The kinetic parameters are then estimated by minimizing the weighted squared error between the noisy TAC and the model output. Standard deviation is computed statistically from the estimated parameters and computed analytically using the framework at each noise level. The ratio of standard deviation to true parameter value obtained from Monte Carlo simulations and analytical computations is compared. Difference between the analytical and MC variance increases with the level of noise and complexity of the compartment model. The standard deviation of the analytical variance also increases with the noise-level and model complexity. The difference between the analytical and MC variance is le...Continue Reading

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