ArticleEJNMMI research2013
FDG kinetic modeling in small rodent brain PET: optimization of data acquisition and analysis.
Article in EJNMMI research, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 16 citations in OpenAlex.
- A robust and versatile deep learning model for prediction of the arterial input function in dynamic small animal [EJNMMI research · 2026Article
- Diffusion-aware compartment model of the cellular uptake of ^{18}F-fluorodeoxyglucose.Physical review. E · 2025Article
- A guideline proposal for mice preparation and care inEJNMMI research · 2022Review
- Optimized Methodology for Reference Region and Image-Derived Input Function Kinetic Modeling in Preclinical PET.IEEE transactions on radiation and plasma medical sciences · 2022Article
- Feasibility of Longitudinal Brain PET with Real-Time Arterial Input Function in Rats.Molecular imaging and biology · 2021Article
- Rapidly (and Successfully) Translating Novel Brain Radiotracers From Animal Research Into Clinical Use.Frontiers in neuroscience · 2020Review
- Reproducibility of compartmental modelling ofEJNMMI physics · 2019Article
- Anesthesia and Preconditioning Induced Changes in Mouse Brain [Molecular imaging and biology · 2019Article
- Functional resting-state brain connectivity is accompanied by dynamic correlations of application-dependent [NeuroImage · 2019Article
- The morphology of the human cerebrovascular system.Human brain mapping · 2018Article
- Determination of the Input Function at the Entry of the Tissue of Interest and Its Impact on PET Kinetic Modeling Parameters.Molecular imaging and biology · 2015Review
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Authors and funding
4 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundKinetic modeling of brain glucose metabolism in small rodents from positron emission tomography (PET) data using 2-deoxy-2-[(18) F]fluoro-d-glucose (FDG) has been highly inconsistent, due to different modeling parameter settings and underestimation of the impact of methodological flaws in experimentation. This article aims to contribute toward improved experimental standards. As solutions for arterial input function (IF) acquisition of satisfactory quality are becoming available for small rodents, reliable two-tissue compartment modeling and the determination of transport and phosphorylation rate constants of FDG in rodent brain are within reach.
methodsData from mouse brain FDG PET with IFs determined with a coincidence counter on an arterio-venous shunt were analyzed with the two-tissue compartment model. We assessed the influence of several factors on the modeling results: the value for the fractional blood volume in tissue, precision of timing and calibration, smoothing of data, correction for blood cell uptake of FDG, and protocol for FDG administration. Kinetic modeling with experimental and simulated data was performed under systematic variation of these parameters.
resultsBlood volume fitting was unreliable and affected the estimation of rate constants. Even small sample timing errors of a few seconds lead to significant deviations of the fit parameters. Data smoothing did not increase model fit precision. Accurate correction for the kinetics of blood cell uptake of FDG rather than constant scaling of the blood time-activity curve is mandatory for kinetic modeling. FDG infusion over 4 to 5 min instead of bolus injection revealed well-defined experimental input functions and allowed for longer blood sampling intervals at similar fit precisions in simulations.
conclusionsFDG infusion over a few minutes instead of bolus injection allows for longer blood sampling intervals in kinetic modeling with the two-tissue compartment model at a similar precision of fit parameters. The fractional blood volume in the tissue of interest should be entered as a fixed value and kinetics of blood cell uptake of FDG should be included in the model. Data smoothing does not improve the results, and timing errors should be avoided by precise temporal matching of blood and tissue time-activity curves and by replacing manual with automated blood sampling.
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