A strategy to account for noise in the X-variable to reduce underestimation in Logan graphical analysis for quantifying receptor density in positron emission tomography

摘要

This study aims to propose an algorithm to improve the accuracy in the quantification of receptor density for parametric imaging in positron emission tomography (PET). By applying parametric imaging methods such as Logan graphical analysis (LGA) and multilinear reference tissue models (MRTM) to PET data, we can acquire the receptor density in terms of the non-displaceable binding potential ( ) index [ ]. LGA is a time-efficient and a computationally efficient algorithm applicable to reversibly binding receptors [ , ]. It transforms data of PET tissue time-activity curves (tTACs) into a simple linear relationship, and can then be estimated from the slope of this relationship using the ordinary least-squares (OLS) method.