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Introduction: Positron Emission Tomography (PET) imaging with selective 18 kDa translocator protein (TSPO) radiotracers has contributed to our understanding on the role of inflammation in disease development and progression. With an increasing number of rodent models of human disease and expansion of the preclinical PET imaging base worldwide, accurate quantification of longitudinal rodent TSPO PET datasets is necessary. This is particularly relevant as TSPO PET quantification relies on invasive blood sampling due to lack of a suitable tissue reference region. Here we investigate the kinetics and quantification bias of a novel TSPO radiotracer [18F]AB5186 in rats using automatic, manual and image derived input functions.
Methods: [18F]AB5186 was administered intravenously and dynamic PET imaging was acquired over 2 hours. Arterial blood was collected manually to derive a population based input function or using an automatic blood sampler to derive a plasma input function. Manually sampled blood was also used to analyze the [18F]AB5186 radiometabolite profile in plasma and applied to all groups as a population based dataset. Kinetic models were used to estimate distribution volumes (VT) and [18F]AB5186 outcome measure bias was determined.
Results: [18F]AB5186 distribution in rats was consistent with TSPO expression and at 2 h post-injection 50% of parent compound was still present in plasma. Population based manual sampling methods and image derived input function (IDIF) underestimated VT by ~50% and 88% compared with automatic blood sampling, respectively. The VT variability was lower when using IDIF versus arterial blood sampling methods and analysis of the Bland-Altman plots showed a good agreement between methods of analysis.
Conclusion: Quantification of TSPO PET rodent data using image-derived methods, which are more amenable for longitudinal scanning of small animals, yields outcome measures with reduced variability and good agreement, albeit biased, compared with invasive blood sampling methods.
Download full-text PDF |
Source |
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6544349 | PMC |
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0217515 | PLOS |
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