Simultaneous Estimation of Input Functions: An Empirical Study

Abstract

In neuroreceptor mapping, methods for the estimation of distribution volume require determination of a metabolite-corrected arterial input function. In application, this may be accomplished by collecting arterial blood samples during scanning, adjusting these measurements according to a separate metabolite analysis, and then modeling the resulting concentration data. Although many groups do this routinely, it is invasive and requires considerable effort. Furthermore, both the plasma and the metabolite data are noisy, and thus estimation of kinetic parameters can be affected by this variability. One promising alternative to full-input function modeling is the simultaneous estimation (SIME) approach, in which kinetic parameters and common input function parameters are estimated using results obtained from several regions at once. We investigate the performance of this approach on data from four different radioligands, using various kinetic models, comparing the results with those obtained by estimation using full-input function modeling. Results indicate that SIME provides a promising alternative for all the radioligands considered.