Estimation of the net influx rate K i and the cerebral metabolic rate of glucose MR glc using a single static [ 18 F]FDG PET scan in rats
Since accurate quantification of 2-deoxy-2- F-fluoro-D-glucose ([ F]FDG) positron emission tomography (PET) requires dynamic acquisition with arterial input function, more practical semi-quantitative (static) approaches are often preferred. However, static standardized uptake values (SUV) are typica...
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Published in: | NeuroImage (Orlando, Fla.) Vol. 233; p. 117961 |
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Main Authors: | , , , , , |
Format: | Journal Article |
Language: | English |
Published: |
United States
Elsevier Limited
01-06-2021
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Subjects: | |
Online Access: | Get full text |
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Summary: | Since accurate quantification of 2-deoxy-2-
F-fluoro-D-glucose ([
F]FDG) positron emission tomography (PET) requires dynamic acquisition with arterial input function, more practical semi-quantitative (static) approaches are often preferred. However, static standardized uptake values (SUV) are typically biased due to large variations in body weight (BW) occurring over time in animal studies. This study aims to improve static [
F]FDG PET SUV quantification by better accounting for BW variations in rats. We performed dynamic [
F]FDG PET imaging with arterial blood sampling in rats (n = 27) with different BW (range 0.230-0.487 kg). By regressing the area under the curve of the input function divided by injected activity against BW (r
=0.697), we determined a conversion factor f(BW) to be multiplied with SUV and SUV
to obtain ratSUV and ratSUV
, providing an improved estimate of the net influx rate K
(r = 0.758, p<0.0001) and cerebral metabolic rate of glucose MR
(r = 0.906, p<0.0001), respectively. In conclusion, the proposed ratSUV and ratSUV
provide a proxy for the K
and MR
based on a single static [
F]FDG PET SUV measurement improving clinical significance and translation of rodent studies. Given a defined strain, sex, age, diet, and weight range, this method is applicable for future experiments by converting SUV with the derived f(BW). |
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ISSN: | 1053-8119 1095-9572 |
DOI: | 10.1016/j.neuroimage.2021.117961 |