Evaluation of methods for generating parametric (R)-[11C]PK11195 binding images

Alie Schuitemaker, Bart N M Van Berckel, Marc A. Kropholler, Reina W. Kloet, Cees Jonker, Philip Scheltens, Adriaan A. Lammertsma, Ronald Boellaard

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Activated microglia can be visualised using (R)-[11C]PK11195 (1-[2-chlorophenyl]-N-methyl-N-[1-methyl-propyl]-3-isoquinoline carboxamide) and positron emission tomography (PET). In previous studies, various methods have been used to quantify (R)-[11C]PK11195 binding. The purpose of this study was to determine which parametric method would be best suited for quantifying (R)-[11C]PK11195 binding at the voxel level. Dynamic (R)-[11C]PK11195 scans with arterial blood sampling were performed in 20 healthy and 9 Alzheimer's disease subjects. Parametric images of both volume of distribution (Vd) and binding potential (BP) were obtained using Logan graphical analysis with plasma input. In addition, BP images were generated using two versions of the basis function implementation of the simplified reference tissue model, two versions of Ichise linearisations, and Logan graphical analysis with reference tissue input. Results of the parametric methods were compared with results of full compartmental analysis using nonlinear regression. Simulations were performed to assess accuracy and precision of each method. It was concluded that Logan graphical analysis with arterial input function is an accurate method for generating parametric images of Vd. Basis function methods, one of the Ichise linearisations and Logan graphical analysis with reference tissue input provided reasonably accurate and precise estimates of BP. In pathological conditions with reduced flow rates or large variations in blood volume, the basis function method is preferred because it produces less bias and is more precise.

Original languageEnglish
Pages (from-to)1603-1615
Number of pages13
JournalJournal of cerebral blood flow and metabolism
Issue number9
Publication statusPublished - 7 Sept 2007


  • Benzodiazepine
  • Benzodiazepine receptor subtypes
  • Compartmental models
  • PET
  • Parametric
  • Radiotracers

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