Article

Kinetic evaluation of [11C]dihydrotetrabenazine by dynamic PET: Measurement of vesicular monoamine transporter

Department of Internal Medicine, University of Michigan, Ann Arbor, USA.
Journal of Cerebral Blood Flow & Metabolism (Impact Factor: 5.34). 12/1996; 16(6):1288-99. DOI: 10.1097/00004647-199611000-00025
Source: PubMed

ABSTRACT (+)-alpha-[11C]Dihydrotetrabenazine (DTBZ) binds to the vesicular monoamine transporter (VMAT2) located in presynaptic vesicles. The purpose of this work was to evaluate various model configurations for analysis of [11C]DTBZ with the aim of providing the optimal measure of monoamine vesicular transporter density obtainable from a single dynamic PET study. PET studies on seven young normal volunteer subjects, ages 20-35, were performed following i.v. injection of 666 +/- 37 MBq (18 +/- 1 mCi) of (+)-alpha-[11C]DTBZ. Dynamic acquisition consisted of a 15-frame sequence over 1 h. Analysis methods included both creation of pixel-by-pixel functional images of transport (K1) and binding (DVtot) and nonlinear least-squares analysis of volume-of-interest data. Pixel-by-pixel calculations were performed for both two-compartment weighted integral calculations and slope-intercept estimations from Logan plots. Nonlinear least-squares analysis was performed applying model configurations with both two-compartments, estimating K1 and DVtot and three compartments, estimating K1-k4. For the more complex configuration, we examined the stability of various binding-related parameters including k3 (konBmax'), k3/k4 (Bmax'/Kd), DVsp[(K1/k2)(k3/k4)], and DVtot [K1/k2(1 + k3/k4)]. The three-compartment model provided significantly improved goodness-of-fit compared to the two-compartment model, yet did not increase the uncertainty in the estimate of the DVtot. Without constraining parameters in the three-compartment model fits, DVtot was found to provide a more stable estimate of binding density than either k3, k3/k4, or DVsp. The two-compartment least-squares analysis yielded approximately 10% underestimations of the total distribution. However, this bias was found to be very consistent from region to region as well as across subjects as indicated by the correlation between two- and three-compartment DVtot estimates of 0.997. We conclude that (+)-alpha-[11C]DTBZ and PET can provide excellent measures of VMAT2 density in the human brain.

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    • "The outcome measure derived from this analysis is BP ND , which refers to the specific to non-specific partition coefficient equal to B max /K D where B max is unoccupied VMAT2 density, and 1/K D is the in vivo affinity of 11 C-DTBZ. The simplified reference tissue model has been shown to be an appropriate model for quantifying DTBZ data in humans without arterial input function (Koeppe et al., 1996; Chan et al., 1999). To address the possibility of volume loss in the patient groups potentially affecting the analysis, partial volume effect correction on time activity curve data was implemented using the Rousset algorithm (Rousset et al., 1998). "
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    Brain 12/2013; 137(2). DOI:10.1093/brain/awt337 · 10.23 Impact Factor
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    • "A few PET ligands have been successfully validated in humans to measure VMAT2 density in vivo. Already quite early, [ 11 C]DTBZ was evaluated by Koeppe et al. (1996) and is still in use today (e.g., Boileau et al. 2010). Another [ 11 C] tetrabenazine (see Canney et al. 1995, for early evaluation of tetrabenazines) that has already been tested in humans over a decade ago is the carbon-labeled methoxytetrabenazine [ 11 C]MTBZ (Vander Borght et al. 1995), but which has, to our knowledge, not been used recently to measure VMAT2 in vivo. "
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    • "However, in many cases it is desirable to perform parametric analysis on pixelwise data, which have much higher noise levels than ROI data. Although parametric imaging has been applied effectively using both linear (Blomqvist, 1984; Blomqvist et al., 1990; Carson et al., 1986; Chen et al., 1998; Feng et al., 1993; Gunn et al., 1997; Koeppe et al., 1996) and nonlinear regression (Herholz, 1987; Huang and Zhou, 1998; Kimura et al., 2002; O'Sullivan, 1994; Zhou et al., 2002c), conventional nonlinear regression is less desirable because it tends to be time consuming and provides parametric images of poor images quality, that is, either too much noise or too much resolution loss if spatial smoothing is applied. To avoid nonlinear regression for SRTM when applying Eq. (2), a basis function method has been developed by sampling discrete values of the nonlinear macroparameter (k 2 /(1ϩBP)) and eliminating the two linear parameters (R 1 and k 2 Ϫ R 1 k 2 /(1 ϩ BP)) by regular linear regression (Gunn et al., 1997; Lawton and Sylvestre, 1971). "
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