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Image-derived input function for human brain using high resolution PET imaging with [C](R)-rolipram and [C]PBR28.

Zanotti-Fregonara P, Liow JS, Fujita M, Dusch E, Zoghbi SS, Luong E, Boellaard R, Pike VW, Comtat C, Innis RB - PLoS ONE (2011)

Bottom Line: Using the image input methods that gave the most accurate results with Logan analysis, we also performed kinetic modelling with a two-tissue compartment model.All methods gave higher scores with [(11)C](R)-rolipram, which has a lower metabolite fraction.Compartment modeling gave less reliable results, especially for the estimation of individual rate constants.

View Article: PubMed Central - PubMed

Affiliation: Molecular Imaging Branch, National Institute of Mental Health (NIMH), National Institutes of Health (NIH), Bethesda, Maryland, United States of America.

ABSTRACT

Background: The aim of this study was to test seven previously published image-input methods in state-of-the-art high resolution PET brain images. Images were obtained with a High Resolution Research Tomograph plus a resolution-recovery reconstruction algorithm using two different radioligands with different radiometabolite fractions. Three of the methods required arterial blood samples to scale the image-input, and four were blood-free methods.

Methods: All seven methods were tested on twelve scans with [(11)C](R)-rolipram, which has a low radiometabolite fraction, and on nineteen scans with [(11)C]PBR28 (high radiometabolite fraction). Logan V(T) values for both blood and image inputs were calculated using the metabolite-corrected input functions. The agreement of image-derived Logan V(T) values with the reference blood-derived Logan V(T) values was quantified using a scoring system. Using the image input methods that gave the most accurate results with Logan analysis, we also performed kinetic modelling with a two-tissue compartment model.

Results: For both radioligands the highest scores were obtained with two blood-based methods, while the blood-free methods generally performed poorly. All methods gave higher scores with [(11)C](R)-rolipram, which has a lower metabolite fraction. Compartment modeling gave less reliable results, especially for the estimation of individual rate constants.

Conclusion: OUR STUDY SHOWS THAT: 1) Image input methods that are validated for a specific tracer and a specific machine may not perform equally well in a different setting; 2) despite the use of high resolution PET images, blood samples are still necessary to obtain a reliable image input function; 3) the accuracy of image input may also vary between radioligands depending on the magnitude of the radiometabolite fraction: the higher the metabolite fraction of a given tracer (e.g., [(11)C]PBR28), the more difficult it is to obtain a reliable image-derived input function; and 4) in association with image inputs, graphical analyses should be preferred over compartmental modelling.

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The concentrations over time of [11C](R)-rolipram (A and B) and [11C]PBR28 (C and D) in plasma from the arterial input function (solid line) and from the image input function (dashed line) of a representative healthy subject.The curves are representative of those from a blood-based (Chen; A and C) and a blood-free (Su; B and D). None of the methods precisely estimated the peak in all the subjects but, in general, blood-based methods yielded a better estimate of the tails of the curves.
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pone-0017056-g003: The concentrations over time of [11C](R)-rolipram (A and B) and [11C]PBR28 (C and D) in plasma from the arterial input function (solid line) and from the image input function (dashed line) of a representative healthy subject.The curves are representative of those from a blood-based (Chen; A and C) and a blood-free (Su; B and D). None of the methods precisely estimated the peak in all the subjects but, in general, blood-based methods yielded a better estimate of the tails of the curves.

Mentions: For both tracers, none of the methods could consistently reproduce the height and shape of the reference arterial peaks. In general, however, the blood-based methods provided a better estimate of the late part (i.e. the tails) of the curves than the blood-free methods (Figure 3).


Image-derived input function for human brain using high resolution PET imaging with [C](R)-rolipram and [C]PBR28.

Zanotti-Fregonara P, Liow JS, Fujita M, Dusch E, Zoghbi SS, Luong E, Boellaard R, Pike VW, Comtat C, Innis RB - PLoS ONE (2011)

The concentrations over time of [11C](R)-rolipram (A and B) and [11C]PBR28 (C and D) in plasma from the arterial input function (solid line) and from the image input function (dashed line) of a representative healthy subject.The curves are representative of those from a blood-based (Chen; A and C) and a blood-free (Su; B and D). None of the methods precisely estimated the peak in all the subjects but, in general, blood-based methods yielded a better estimate of the tails of the curves.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3045425&req=5

pone-0017056-g003: The concentrations over time of [11C](R)-rolipram (A and B) and [11C]PBR28 (C and D) in plasma from the arterial input function (solid line) and from the image input function (dashed line) of a representative healthy subject.The curves are representative of those from a blood-based (Chen; A and C) and a blood-free (Su; B and D). None of the methods precisely estimated the peak in all the subjects but, in general, blood-based methods yielded a better estimate of the tails of the curves.
Mentions: For both tracers, none of the methods could consistently reproduce the height and shape of the reference arterial peaks. In general, however, the blood-based methods provided a better estimate of the late part (i.e. the tails) of the curves than the blood-free methods (Figure 3).

Bottom Line: Using the image input methods that gave the most accurate results with Logan analysis, we also performed kinetic modelling with a two-tissue compartment model.All methods gave higher scores with [(11)C](R)-rolipram, which has a lower metabolite fraction.Compartment modeling gave less reliable results, especially for the estimation of individual rate constants.

View Article: PubMed Central - PubMed

Affiliation: Molecular Imaging Branch, National Institute of Mental Health (NIMH), National Institutes of Health (NIH), Bethesda, Maryland, United States of America.

ABSTRACT

Background: The aim of this study was to test seven previously published image-input methods in state-of-the-art high resolution PET brain images. Images were obtained with a High Resolution Research Tomograph plus a resolution-recovery reconstruction algorithm using two different radioligands with different radiometabolite fractions. Three of the methods required arterial blood samples to scale the image-input, and four were blood-free methods.

Methods: All seven methods were tested on twelve scans with [(11)C](R)-rolipram, which has a low radiometabolite fraction, and on nineteen scans with [(11)C]PBR28 (high radiometabolite fraction). Logan V(T) values for both blood and image inputs were calculated using the metabolite-corrected input functions. The agreement of image-derived Logan V(T) values with the reference blood-derived Logan V(T) values was quantified using a scoring system. Using the image input methods that gave the most accurate results with Logan analysis, we also performed kinetic modelling with a two-tissue compartment model.

Results: For both radioligands the highest scores were obtained with two blood-based methods, while the blood-free methods generally performed poorly. All methods gave higher scores with [(11)C](R)-rolipram, which has a lower metabolite fraction. Compartment modeling gave less reliable results, especially for the estimation of individual rate constants.

Conclusion: OUR STUDY SHOWS THAT: 1) Image input methods that are validated for a specific tracer and a specific machine may not perform equally well in a different setting; 2) despite the use of high resolution PET images, blood samples are still necessary to obtain a reliable image input function; 3) the accuracy of image input may also vary between radioligands depending on the magnitude of the radiometabolite fraction: the higher the metabolite fraction of a given tracer (e.g., [(11)C]PBR28), the more difficult it is to obtain a reliable image-derived input function; and 4) in association with image inputs, graphical analyses should be preferred over compartmental modelling.

Show MeSH