Model Error Correction for Linear Methods of Reversible Radioligand Binding Measurements in PET Studies

TL;DR

This paper analyzes the bias in graphical PET quantification methods caused by model error, derives conditions for over- or under-estimation, and proposes a new model that reduces bias and improves accuracy.

Contribution

It introduces a new model and numerical algorithm that mitigate bias from model error in graphical analysis of PET data, outperforming existing methods.

Findings

Bias is inversely proportional to dissociation rate.

Proposed method reduces bias significantly.

Achieves comparable accuracy with shorter scan durations.

Abstract

Graphical analysis methods are widely used in positron emission tomography quantification because of their simplicity and model independence. But they may, particularly for reversible kinetics, lead to bias in the estimated parameters. The source of the bias is commonly attributed to noise in the data. Assuming a two-tissue compartmental model, we investigate the bias that originates from model error. This bias is an intrinsic property of the simplified linear models used for limited scan durations, and it is exaggerated by random noise and numerical quadrature error. Conditions are derived under which Logan's graphical method either over- or under-estimates the distribution volume in the noise-free case. The bias caused by model error is quantified analytically. The presented analysis shows that the bias of graphical methods is inversely proportional to the dissociation rate. Furthermore, visual examination of the linearity of the Logan plot is not sufficient for guaranteeing that equilibrium has been reached. A new model which retains the elegant properties of graphical analysis methods is presented, along with a numerical algorithm for its solution. We perform simulations with the fibrillar amyloid-beta radioligand [11C] benzothiazole-aniline using published data from the University of Pittsburgh and Rotterdam groups. The results show that the proposed method significantly reduces the bias due to model error. Moreover, the results for data acquired over a 70 minutes scan duration are at least as good as those obtained using existing methods for data acquired over a 90 minutes scan duration.