Using a reference tissue model with spatial constraint to quantify [11C]Pittsburgh compound B PET for early diagnosis of Alzheimer's disease

Abstract

Introduction:Reference tissue model (RTM) is a compartmental modeling approach that uses reference tissue time activity curve (TAC) as input for quantification of ligand–receptor dynamic PET without blood sampling. There are limitations in applying the RTM for kinetic analysis of PET studies using [11C]Pittsburgh compound B ([11C]PIB). For region of interest (ROI) based kinetic modeling, the low specific binding of [11C]PIB in a target ROI can result in a high linear relationship between the output and input. This condition may result in amplification of errors in estimates using RTM. For pixel-wise quantification, due to the high noise level of pixel kinetics, the parametric images generated by RTM with conventional linear or nonlinear regression may be too noisy for use in clinical studies. Methods:We applied RTM with parameter coupling and a simultaneous fitting method as a spatial constraint for ROI kinetic analysis. Three RTMs with parameter coupling were derived from a classical compartment model with plasma input: an RTM of 4 parameters (R1, k′2R, k4, BP) (RTM4P); an RTM of 5 parameters (R1, k2R, NS, k6, BP) (RTM5P); and a simplified RTM (SRTM) of 3 parameters (R1, k′2R, BP) (RTM3P). The parameter sets [k′2R, k4], [k2R, NS, k6], and k′2R are coupled among ROIs for RTM4P, RTM5P, and RTM3P, respectively. A linear regression with spatial constraint (LRSC) algorithm was applied to the SRTM for parametric imaging. Logan plots were used to estimate the distribution volume ratio (DVR) (=1+BP (binding potential)) in ROI and pixel levels. Ninety-minute [11C]PIB dynamic PET was performed in 28 controls and 6 individuals with mild cognitive impairment (MCI) on a GE Advance scanner. ROIs of cerebellum (reference tissue) and 15 other regions were defined on coregistered MRIs. Results:The coefficients of variation of DVR estimates from RTM3P obtained by the simultaneous fitting method were lower by 77–89% (in striatum, frontal, occipital, parietal, and cingulate cortex) as compared to that by conventional single ROI TAC fitting method. There were no significant differences in both TAC fitting and DVR estimates between the RTM3P and the RTM4P or RTM5P. The DVR in striatum, lateral temporal, frontal and cingulate cortex for MCI group was 25% to 38% higher compared to the control group (p≤0.05), even in this group of individuals with generally low PIB retention. The DVR images generated by the SRTM with LRSC algorithm had high linear correlations with those from the Logan plot (R2=0.99). Conclusion:In conclusion, the RTM3P with simultaneous fitting method is shown to be a robust compartmental modeling approach that may be useful in [11C]PIB PET studies to detect early markers of Alzheimer's disease where specific ROIs have been hypothesized. In addition, the SRTM with LRSC algorithm may be useful in generating R1 and DVR images for pixel-wise quantification of [11C]PIB dynamic PET.