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. 2015 Nov;35(11):1771-82.
doi: 10.1038/jcbfm.2015.120. Epub 2015 Jun 10.

Quantification of [(11)C]PIB PET for imaging myelin in the human brain: a test-retest reproducibility study in high-resolution research tomography

Affiliations

Quantification of [(11)C]PIB PET for imaging myelin in the human brain: a test-retest reproducibility study in high-resolution research tomography

Mattia Veronese et al. J Cereb Blood Flow Metab. 2015 Nov.

Abstract

An accurate in vivo measure of myelin content is essential to deepen our insight into the mechanisms underlying demyelinating and dysmyelinating neurological disorders, and to evaluate the effects of emerging remyelinating treatments. Recently [(11)C]PIB, a positron emission tomography (PET) tracer originally conceived as a beta-amyloid marker, has been shown to be sensitive to myelin changes in preclinical models and humans. In this work, we propose a reference-region methodology for the voxelwise quantification of brain white-matter (WM) binding for [(11)C]PIB. This methodology consists of a supervised procedure for the automatic extraction of a reference region and the application of the Logan graphical method to generate distribution volume ratio (DVR) maps. This approach was assessed on a test-retest group of 10 healthy volunteers using a high-resolution PET tomograph. The [(11)C]PIB PET tracer binding was shown to be up to 23% higher in WM compared with gray matter, depending on the image reconstruction. The DVR estimates were characterized by high reliability (outliers <1%) and reproducibility (intraclass correlation coefficient (ICC) >0.95). [(11)C]PIB parametric maps were also found to be significantly correlated (R(2)>0.50) to mRNA expressions of the most represented proteins in the myelin sheath. On the contrary, no correlation was found between [(11)C]PIB imaging and nonmyelin-associated proteins.

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Figures

Figure 1
Figure 1
Impact of the reconstruction setting on positron emission tomography (PET) image quality. The figure shows an example of PET images obtained from the same acquisition by modifying the reconstruction setting: Poisson ordered subset expectation maximization algorithm (POSEM) 1, 2, 3, 4, 5, 6, 10 iterations and POSEM with 10 iterations combined with point spread function (PSF) smoothing. Maps refer to the same transaxial slice, 60 minutes after tracer injection, obtained from one representative subject of the study. Trend of SNR (SNR=mean/s.d. of the voxel activity) levels calculated from each image is reported in the bar graph as function of reconstruction setting. SNR, signal-to-noise ratio.
Figure 2
Figure 2
Impact of reconstruction setting on reference region extraction. (A) Spatial distribution of the voxels selected via supervised clustering as a reference region with different Poisson ordered subset expectation maximization algorithm (POSEM) reconstruction settings (1 and 10 iterations, respectively) is shown. (B and C) The time courses of the regional activity obtained from different reconstructed positron emission tomography (PET) images, with POSEM 1-iteration and POSEM 10-iterations, respectively, are shown. Notably, the two reconstruction settings are associated with the highest and lowest image signal-to-noise ratio (SNR) (Figure 1). (D) The fraction of the gray matter (GM) voxels selected as a reference region as function of the reconstruction settings is reported. (E) The normalized TACs of the three kinetic classes used by the supervised clustering in comparison with the normalized reference region time-activity curves (TACs) obtained with POSEM 1-iteration (red line) and POSEM 10-iterations (blue line), respectively, are shown.
Figure 3
Figure 3
Quantification results. (A and B) Mean and variability across subjects of LOGAN distribution volume ratio (DVR) and standardized uptake value (SUV) estimates as a function of reconstruction setting are reported. Gray bars indicate gray matter (GM), white bars indicate white matter (WM), and black bars indicate basal ganglia (BG). The mean and variability of WM/GM relative differences are also shown. An example of parametric maps computed with both SUV and LOGAN at different axial levels for a representative subject is reported in (C).
Figure 4
Figure 4
Local analysis: contrast and reproducibility. The mean and s.d. of white matter (WM)/gray matter (GM) contrast values for Logan distribution volume ratio (DVR) and standardized uptake value (SUV) analysis are reported as function of the positron emission tomography (PET) reconstruction setting and simulated region dimensions (A and B, respectively). Similarly, the mean local test/retest reproducibility is also shown for both GM and WM as obtained with Logan (C and E) and SUV (D and F).
Figure 5
Figure 5
Local analysis: performance index. The mean and s.d. of the p-index values for Logan and standardized uptake value (SUV) analysis are reported as function of the positron emission tomography (PET) reconstruction setting and simulated region dimensions. Results refer to white-matter normalization for Logan (A) and SUV (B), respectively. Equivalent results are obtained with gray-matter normalization (data not shown).
Figure 6
Figure 6
[11C]PIB uptake and partial volume effect. (A) The mean distribution volume ratio (DVR) values measured (open circles) and modelled (dashed lines) for WM and GM, respectively, as function of the distance from the cerebral spinal fluid (CSF) are shown. Data refer to Poisson ordered subset expectation maximization algorithm (POSEM) 10 iterations with point spread function (PSF) smoothing reconstruction setting. (B) The correspondent [11C]PIB uptake map based on the distance model from CSF is shown. Hyperintense regions refer to those voxels with the highest DVR estimates.
Figure 7
Figure 7
Comparison between [11C]PIB imaging and myelin-associated protein gene expressions. (A) The myelin-associated protein mRNA auto-correlations are shown. Probe selection and their spatial expression patterns are used as reported by the Allen Brain Atlas database (http://human.brain-map.org/). (B) Reports for the same probes the relative differences of mRNA expressions between white matter (WM) and gray matter (GM). (C) mRNA cross-correlation with myelin-associated templates obtained by using Poisson ordered subset expectation maximization algorithm (POSEM) 10 iterations with point spread function (PSF) smoothing reconstruction setting is shown. Black and gray bars refer to correlation with [11C]PIB myelin maps using distribution volume ratio (DVR) and standardized uptake value (SUV), respectively.

References

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