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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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Transaxial slices from a [11C](R)-rolipram brain scan of a healthy volunteer and from a simulated study using a digital phantom.Upper row: [11C](R)-rolipram images across the thalamus summed over the whole duration of the scan from a phantom (A) and a healthy volunteer (B). The phantom images are realistic and quite similar to those from the real subjects. The external rim of activity surrounding the brain, in both the subject and the phantom, is scalp activity. Middle row: images summed over the first two minutes at the carotid level. The carotids are well visible near the temporal lobes for both the phantom (C) and the healthy volunteer (D). The regions of high activity visible in the lower part of the cerebellum of the subject (D) are the cerebellar venous sinuses (not simulated in the phantom studies). Bottom row: late images (three summed frames taken at about 1 hour after injection) from a phantom (E) and a subject (F). At late times the carotids are not well visible anymore and the spill-over effect from surrounding tissues becomes more important.
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pone-0017056-g001: Transaxial slices from a [11C](R)-rolipram brain scan of a healthy volunteer and from a simulated study using a digital phantom.Upper row: [11C](R)-rolipram images across the thalamus summed over the whole duration of the scan from a phantom (A) and a healthy volunteer (B). The phantom images are realistic and quite similar to those from the real subjects. The external rim of activity surrounding the brain, in both the subject and the phantom, is scalp activity. Middle row: images summed over the first two minutes at the carotid level. The carotids are well visible near the temporal lobes for both the phantom (C) and the healthy volunteer (D). The regions of high activity visible in the lower part of the cerebellum of the subject (D) are the cerebellar venous sinuses (not simulated in the phantom studies). Bottom row: late images (three summed frames taken at about 1 hour after injection) from a phantom (E) and a subject (F). At late times the carotids are not well visible anymore and the spill-over effect from surrounding tissues becomes more important.

Mentions: In total, the dynamic PET phantom was computed by linear combination of the phantom structures, weighted by the associated kinetics, and sampled into time frames whose number and duration time were identical to those of the clinical studies (see below). The dynamic phantom was forward projected and noise was added, taking into account scattered and random coincidences (Figure 1). Image-input was calculated for all phantoms using the methods of Chen and Mourik. The fraction of unchanged parent was derived by multiplying the “arterial” and image input of the phantoms by the average parent/whole blood time activity curve measured in the clinical scans, after linear interpolation of the blood data to match the PET time schedule.


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)

Transaxial slices from a [11C](R)-rolipram brain scan of a healthy volunteer and from a simulated study using a digital phantom.Upper row: [11C](R)-rolipram images across the thalamus summed over the whole duration of the scan from a phantom (A) and a healthy volunteer (B). The phantom images are realistic and quite similar to those from the real subjects. The external rim of activity surrounding the brain, in both the subject and the phantom, is scalp activity. Middle row: images summed over the first two minutes at the carotid level. The carotids are well visible near the temporal lobes for both the phantom (C) and the healthy volunteer (D). The regions of high activity visible in the lower part of the cerebellum of the subject (D) are the cerebellar venous sinuses (not simulated in the phantom studies). Bottom row: late images (three summed frames taken at about 1 hour after injection) from a phantom (E) and a subject (F). At late times the carotids are not well visible anymore and the spill-over effect from surrounding tissues becomes more important.
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3045425&req=5

pone-0017056-g001: Transaxial slices from a [11C](R)-rolipram brain scan of a healthy volunteer and from a simulated study using a digital phantom.Upper row: [11C](R)-rolipram images across the thalamus summed over the whole duration of the scan from a phantom (A) and a healthy volunteer (B). The phantom images are realistic and quite similar to those from the real subjects. The external rim of activity surrounding the brain, in both the subject and the phantom, is scalp activity. Middle row: images summed over the first two minutes at the carotid level. The carotids are well visible near the temporal lobes for both the phantom (C) and the healthy volunteer (D). The regions of high activity visible in the lower part of the cerebellum of the subject (D) are the cerebellar venous sinuses (not simulated in the phantom studies). Bottom row: late images (three summed frames taken at about 1 hour after injection) from a phantom (E) and a subject (F). At late times the carotids are not well visible anymore and the spill-over effect from surrounding tissues becomes more important.
Mentions: In total, the dynamic PET phantom was computed by linear combination of the phantom structures, weighted by the associated kinetics, and sampled into time frames whose number and duration time were identical to those of the clinical studies (see below). The dynamic phantom was forward projected and noise was added, taking into account scattered and random coincidences (Figure 1). Image-input was calculated for all phantoms using the methods of Chen and Mourik. The fraction of unchanged parent was derived by multiplying the “arterial” and image input of the phantoms by the average parent/whole blood time activity curve measured in the clinical scans, after linear interpolation of the blood data to match the PET time schedule.

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