Voxel level quantification of [11C]CURB, a radioligand for Fatty Acid Amide Hydrolase, using high resolution positron emission tomography

[11C]CURB is a novel irreversible radioligand for imaging fatty acid amide hydrolase in the human brain. In the present work, we validate an algorithm for generating parametric map images of [11C]CURB acquired with a high resolution research tomograph (HRRT) scanner. This algorithm applies the basis function method on an irreversible two-tissue compartment model (k4 = 0) with arterial input function, i.e., BAFPIC. Monte Carlo simulations are employed to assess bias and variability of the binding macroparameters (Ki and λk3) as a function of the voxel noise level and the range of basis functions. The results show that for a [11C]CURB time activity curve with noise levels corresponding to a voxel of an image acquired with the HRRT and reconstructed with the filtered back projection algorithm, the implementation of BAFPIC requires the use of a constant vascular fraction of tissue (5%) and a cutoff for slow frequencies (0.06 min-1). With these settings, BAFPIC maintains the probabilistic distributions of the binding macroparameters with approximately Gaussian shape and minimizes the bias and variability for large physiological ranges of the rate constants of [11C]CURB. BAFPIC reduces the variability of Ki to a third of that given by Patlak plot, the standard graphical method for irreversible radioligands. Application to real data demonstrated an excellent correlation between region of interest and BAFPIC parametric data and agreed with the simulations results. Therefore, BAFPIC with a constant vascular fraction can be used to generate parametric maps of [11C]CURB images acquired with an HRRT provided that the limits of the basis functions are carefully selected.