Multi-input spectral analysis for assessing cerebral uptake of labelled metabolites: Validation and application to [11C]PIB and [18F]FDDNP studies

Mark Lubberink*, B. N M Van Berckel, Gert Luurtsema, Kevin Takkenkamp, Nelleke Tolboom, Maqsood Yaqub, Adriaan A. Lammertsma

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


Background and aims: Cerebral uptake of radioactive metabolites poses a major challenge for quantitative tracer kinetic PET studies. Although this uptake can be quantified by PET studies of the labelled metabolites themselves [1,2], it requires extensive chemistry and additional PET measurements, and is not feasible on a routine basis. Use of spectral analysis [3] has previously been suggested as a tool for assessing uptake of labelled metabolites of (R)-[11C]verapamil [4]. The aim of the present study was to investigate the potential use of multi-input spectral analysis (MISA) for assessment of metabolite uptake using simulations. In addition, the method was applied to clinical [18F]FDDNP and [11C]PIB data. Methods: MISA was implemented using 30 basis functions (BFs) based on tracer concentration in plasma, 30 BFs based on metabolite concentration in plasma, and one whole blood compartment, with time constants between 1/scan length and 1 min-1. Sets of 1000 noisy time-activity curves (TACs) were simulated, each set being based on different rate constants and plasma kinetic data with fast ([11C]PIB) or slow ((R)-[11C]verapamil) metabolism, with or without metabolite uptake. Volume of distribution (Vd), model order, and percent area under the curve due to metabolites (%AUCmet) were determined using MISA and compared to true values. TACs of 35 grey matter regions of interest (ROIs) of 10 subjects (5 [18F]FDDNP and 5 [11C]PIB) were analysed using MISA to identify possible metabolite uptake. Results: Using BFs based on [11C]PIB plasma kinetics, good correlation (r2 0.96) was found between %AUCmet obtained by MISA and true %AUCmet. MISA derived %AUCmet significantly > 0 always corresponded to a true metabolite compartment (figure 1). A metabolite compartment was consistently identified in clinical [18F]FDDNP time-activity curves, with mean ± SD %AUCmet of 29 ± 10 in 175 ROIs, compared to 22 ± 4 for [11C]PIB. According to the Akaike criterion, MISA consistently provided better fits than SA with only parent BFs in the case of [18F]FDDNP. Conclusions: Simulations indicate that a potential metabolite contribution can be identified accurately using MISA for tracers with fast metabolism. MISA applied to clinical data revealed likely metabolite uptake for [18F]FDDNP and [11C]PIB. Further validation of the method, using tracers for which metabolite kinetics have been measured such as [18F]altanserin [1], will be subject of future studies.

Original languageEnglish
JournalJournal of Cerebral Blood Flow and Metabolism
Issue numberSUPPL. 1
Publication statusPublished - 13 Nov 2007

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