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Reproducibility of Quantitative Brain Imaging Using a PET-Only and a Combined PET/MR System

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The purpose of this study was to test the feasibility of migrating a quantitative brain imaging protocol from a positron emission tomography (PET)-only system to an integrated PET/MR system. Potential differences in both absolute radiotracer concentration as well as in the derived kinetic parameters as a function of PET system choice have been investigated. Five healthy volunteers underwent dynamic (R)-[11C]verapamil imaging on the same day using a GE-Advance (PET-only) and a Siemens Biograph mMR system (PET/MR). PET-emission data were reconstructed using a transmission-based attenuation correction (AC) map (PET-only), whereas a standard MR-DIXON as well as a low-dose CT AC map was applied to PET/MR emission data. Kinetic modeling based on arterial blood sampling was performed using a 1-tissue-2-rate constant compartment model, yielding kinetic parameters (K1and k2) and distribution volume (V T ). Differences for parametric values obtained in the PET-only and the PET/MR systems were analyzed using a 2-way Analysis of Variance (ANOVA). Comparison of DIXON-based AC (PET/MR) with emission data derived from the PET-only system revealed average inter-system differences of -33 ± 14% (p< 0.05) for the K1parameter and -19 ± 9% (p< 0.05) for k2. Using a CT-based AC for PET/MR resulted in slightly lower systematic differences of -16 ± 18% for K1and -9 ± 10% for k2. The average differences in V T were -18 ± 10% (p< 0.05) for DIXON- and -8 ± 13% for CT-based AC. Significant systematic differences were observed for kinetic parameters derived from emission data obtained from PET/MR and PET-only imaging due to different standard AC methods employed. Therefore, a transfer of imaging protocols from PET-only to PET/MR systems is not straightforward without application of proper correction methods.Clinical Trial, identifier 2013-001724-19.

Original languageEnglish
JournalFrontiers in Neuroscience
Pages (from-to)396
Publication statusPublished - 2017

    Research areas

  • Journal Article

ID: 52704258