A non-linear mixed effect modelling approach for metabolite correction of the arterial input function in PET studies

Quantitative PET studies with arterial blood sampling usually require the correction of the measured total plasma activity for the presence of metabolites. In particular, if labelled metabolites are found in the plasma in significant amounts their presence has to be accounted for, because it is the concentration of the parent tracer which is required for data quantification. This is achieved by fitting a Parent Plasma fraction (PPf) model to discrete metabolite measurements. The commonly used method is based on an individual approach, i.e. for each subject the PPf model parameters are estimated from its own metabolite samples, which are, in general, sparse and noisy. This fact can compromise the quality of the reconstructed arterial input functions, and, consequently, affect the quantification of tissue kinetic parameters. In this study, we proposed a Non-Linear Mixed Effect Modelling (NLMEM) approach to describe metabolite kinetics. Since NLMEM has been developed to provide robust parameter estimates in the case of sparse and/or noisy data, it has the potential to be a reliable method for plasma metabolite correction. Three different PET datasets were considered: [11C]-(+)-PHNO (54 scans), [11C]-PIB (22 scans) and [11C]-DASB (30 scans). For each tracer both simulated and measured data were considered and NLMEM performance was compared with that provided by individual analysis. Results showed that NLMEM provided improved estimates of the plasma parent input function over the individual approach when the metabolite data were sparse or contained outliers

A non linear mixed effect modelling approach for metabolite correction of the arterial input function in pet studies

Quantitative PET studies with arterial blood sampling usually require the correction of the measured total plasma activity for the presence of metabolites. In particular, if labelled metabolites are found in the plasma in significant amounts their presence has to be accounted for, because it is the concentration of the parent tracer which is required for data quantification. This is achieved by fitting a Parent Plasma fraction (PPf) model to discrete metabolite measurements. The commonly used method is based on an individual approach, i.e. for each subject the PPf model parameters are estimated from its own metabolite samples, which are, in general, sparse and noisy. This fact can compromise the quality of the reconstructed arterial input functions, and, consequently, affect the quantification of tissue kinetic parameters. In this study, we proposed a Non-Linear Mixed Effect Modelling (NLMEM) approach to describe metabolite kinetics. Since NLMEM has been developed to provide robust parameter estimates in the case of sparse and/or noisy data, it has the potential to be a reliable method for plasma metabolite correction. Three different PET datasets were considered: [11C]-(+)-PHNO (54 scans), [11C]-PIB (22 scans) and [11C]-DASB (30 scans). For each tracer both simulated and measured data were considered and NLMEM performance was compared with that provided by individual analysis. Results showed that NLMEM provided improved estimates of the plasma parent input function over the individual approach when the metabolite data were sparse or contained outliers

Kinetic Analysis of Dynamic PET for Molecular, Functional and Physiological Characterization of Diseases

PET with targeted probes may better elucidate the molecular and functional basis of diseases. The widely used standardized uptake value from static imaging, however, cannot quantify the probe uptake processes like perfusion, permeability, binding to and disassociation (k4) from target. The overarching thesis goal is to develop a model to enable kinetic analysis of dynamic imaging to separate these processes. As perfusion delivery is not modelled in the current standard two tissue compartment (S2TC) model, I developed a flow modified two tissue compartment (F2TC) model that incorporates the blood flow effect. The model’s performances were investigated with simulation. It was applied to derive kinetic parameters of [18F]FAZA binding to highly hypoxic pancreatic cancer. As a validation, the distribution volume (DV) of [18F]FAZA determined with the F2TC and S2TC model were compared with graphical analysis (GA). Kinetic analysis requires arterial concentration of the native probe to model the observed tissue uptake over time, therefore, a method was developed to correct for the metabolite contamination of arterial plasma. Based on fractional Euclidean distance of estimated and simulated parameters, F2TC model performed better than S2TC model, particularly with longer mean transit time due to the neglect of perfusion effect in the latter model. Also, dynamic acquisition longer than 45 minutes did not improve the accuracy of estimated F2TC model parameters. In the pancreatic cancer study: (a) GA showed that [18F]FAZA was reversibly bound to hypoxic cells; (b) DV estimated by the F2TC and S2TC model was not and was significantly different from GA respectively; (c) k4 and DV estimated by F2TC model could distinguish normal and cancerous tissue with 95% sensitivity. TLC-autoradiography identified metabolites in 2µL of arterial plasma with radioactivity as low as 17Bq. This high sensitivity and the ability to measure multiple (8-12) samples simultaneously could allow metabolite correction of arterial plasma to be performed in individual studies. Finally, the reversible binding of [18F]FAZA in hypoxic pancreatic tumor cells could be due to efflux of reduced products by the multidrug resistance protein. Therefore, kinetic analysis of dynamic [18F]FAZA PET could monitor both hypoxia and drug resistance for individualized treatment