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Neuroimage. 2013 Feb 1;66:611-22. doi: 10.1016/j.neuroimage.2012.10.048. Epub 2012 Oct 27.

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

Author information

1
Department of Information Engineering, University of Padova, Padova, Italy.
2
Imanova Limited, London, UK; Department of Medicine, Imperial College London, London, UK; Department of Engineering Science, University of Oxford, Oxford, UK.
3
Clinical Pharmacology, Modelling and Simulation, GlaxoSmithKline, Stockley Park, UK.
4
Department of Information Engineering, University of Padova, Padova, Italy. Electronic address: bertoldo@dei.unipd.it.

Abstract

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.

KEYWORDS:

Metabolite correction; Non-Linear Mixed Effects Modelling; Quantitative positron emission tomography

[Indexed for MEDLINE]

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