A statistical method for the analysis of positron emission tomography neuroreceptor ligand data

Abstract

A method for voxel by voxel statistical inference of PET radioligand receptor studies is presented. This method is aimed at detecting differences in radioligand binding between baseline and activation scans. It uses nonlinear least squares theory to estimate the ligand-receptor model parameters and utilizes the residuals to calculate their associated variance. The approach both increases the degrees of freedom for statistical testing and produces more accurate estimates of the standard deviation of the parameters. This technique is applicable to any ligand with a validated compartmental model, whether reversibly or irreversibly bound. The method was investigated and compared with a simple voxel-wise t test. Both simulated and real PET data for the dopamine D(1) receptor ligand [(11)C]SCH 23390 were used to assess the method. The assumptions implicit in the residuals methods were validated. The residuals method was found to be more sensitive than a simple t test, while not producing false-positive results. In addition, we showed that this method reliably differentiates changes in radioligand binding from the effects of changes in cerebral blood flow.