Background: A new model of an input function for human [(18)F]-2-Deoxy-2-fluoro-d-glucose fluoro (FDG) positron emission tomography (PET) brain studies with bolus injection is presented.
Methods: Input data for early time, roughly up to 0.6 min, were obtained noninvasively from the time-activity curve (TAC) measured from a carotid artery region of interest. Representative tissue TACs were obtained by clustering the output curves to a limited number of dominant clusters. Three venous plasma samples at a later time were used to fit the functional form of the input function in conjunction with obtaining kinetic rate parameters of the dominant clusters, K(1), k(2) and k(3), using the compartmental model for FDG-PET. Experiments to test the approach used data from 18 healthy subjects.
Results: The model provides an effective means to recover the input function in FDG-PET studies. Weighted nonlinear least squares parameter estimation using the recovered input function, as contrasted with use of plasma samples, yielded highly correlated values of K=K(1)k(3)/(k(2)+k(3)) for simulated data, a correlation coefficient of 0.99780, a slope of 1.019 and an intercept of almost zero. The estimates of K for real data by graphical Patlak analysis using the recovered input function were almost identical to those obtained using arterial plasma samples, with correlation coefficients greater than 0.9976, regression slopes between 0.958 and 1.091 and intercepts that are virtually zero.
Conclusions: A reliable semiautomated alternative for input function estimation that uses image-derived data augmented with three plasma samples is presented and evaluated for FDG-PET human brain studies.