Improving PET receptor binding estimates from Logan plots using principal component analysis

This work reports a principal component analysis (PCA)-based approach for reducing bias in distribution volume ratio (DVR) estimates from Logan plots in positron emission tomography (PET). Comparison has been made of all existing bias-removal methods with the proposed PCA method, for both single-estimate PET studies and intervention studies where pre- and post-intervention estimates are made. Bias in Logan-based DVR estimates is because of the noise in the PET time-activity curves (TACs) that propagates as correlated errors in dependent and independent variables of the Logan equation. Intervention studies show this same bias but also higher variance in DVR estimates. In this work, noise in the TACs was reduced by fitting the curves to a low-dimension PCAbased linear model, leading to reduced bias and variance in DVR. For validating the approach, TACs with realistic noise were simulated for a C-11-labeled tracer with carfentanil (CFN)-like kinetics for both single-measurement and intervention studies. Principal component analysis and existing methods were applied to the simulated data and their performance was compared by statistical analysis. The results indicated that existing methods either removed only part of the bias or reduced bias at the expense of precision. The proposed method removed similar to 90% of the bias while also improving precision in both single-and dual-measurement simulations. After validation of the proposed method in simulations, PCA, along with the existing methods, was applied to human [C-11] CFN data acquired for both single estimation of DVR and dual-estimation intervention studies. Similar results were observed in human scans as were seen in the simulation studies.