Sage Journals: Discover world-class research

Abstract

The development of high-affinity radiotracers for positron emission tomography (PET) has allowed for quantification of dopamine receptors in extrastriatal and striatal regions of the brain. As these new radiotracers have distinctly different kinetic properties than their predecessors, it is important to examine the suitability of kinetic models to represent their uptake, distribution, and in vivo washout. Using the simplified reference tissue model, we investigated the influence of reference region choice on the striatal binding potential of ¹⁸F-fallypride, a high-affinity dopamine D₂/D₃ receptor ligand. We compared the use of the visual cortex and a white matter region (superior longitudinal fasciculus) to the cerebellum, a commonly used reference tissue, in a PET-fallypride study of healthy and methamphetamine-dependent subjects. Compared to the cerebellum, use of the visual cortex produced significantly greater sample variance in binding potential relative to nondisplaceable uptake (BP_ND). Use of the white matter region was associated with BP_ND values and sample variance similar to those obtained with the cerebellum and a larger effect size for the group differences in striatal BP_ND between healthy and methamphetamine-dependent subjects. Our results do not support the use of the visual cortex as a reference region in ¹⁸F-fallypride studies and suggest that white matter may be a reasonable alternative to the cerebellum as it displays similar statistical and kinetic properties.

ASSESSMENT OF DOPAMINE RECEPTORS as biomarkers of dopaminergic signaling in the brain is relevant to numerous neuropsychiatric conditions, such as Parkinson disease, drug use disorders, and schizophrenia. With the development of high-affinity radiotracers for dopamine receptors, it is now possible to quantify dopamine receptor binding using positron emission tomography (PET) in areas outside the striatum, such as the thalamus, amygdala, and cerebral cortex, where dopamine receptor densities are lower than in the striatum. The advent of new radiotracers with binding and kinetic properties that differ distinctly from those of earlier radiotracers, however, has made it important to evaluate the kinetic modeling methods used to describe the uptake, distribution, and in vivo washout of these tracers.

The reference tissue model (RTM) was developed for autoradiography and PET to simplify the standard quantification method for radiotracer kinetic analysis, which requires an arterial plasma input function based on radioactivity of the free radiotracer (not the protein-bound parent compound or metabolites).^1–3 This two-tissue compartment model (four parameters) was originally applied to, and validated for, tracers with relatively fast kinetics and modest receptor binding potentials (BP_ND: binding potential relative to nondisplaceable uptake) in the range of 1 to 5 (eg, ³H-diprenorphine and ¹¹C-raclopride). Although the RTM produces large BP_ND values, it converges slowly (often not at all), and the standard errors of the parameters estimated in the model (R₁, k₂, and k₃) tend to be large.⁴ A subsequent simplification of the RTM method, the simplified reference tissue model (SRTM),⁴ was developed to address these problems. The SRTM is a single-tissue compartmental model (three parameters) applicable only to radiotracers that produce regional time-radioactivity curves (TACs) that can be fit satisfactorily to a single exponential form. The SRTM was validated in human subjects for ¹¹C-SCH23390 and ¹¹C-raclopride, tracers that meet this requirement.⁴ However, a simulation study of the binding of ¹¹C-WAY-100635,⁵ which has slower kinetics and higher affinity, showed that estimates of BP_ND obtained with SRTM can have substantial errors.

RTMs have also been applied to ¹¹C- and ¹⁸F-labeled receptor ligands that exhibit relatively slow kinetics and yield very high values of BP_ND (in the range of 10 to 40). Striatal BP_ND values for these tracers in different laboratories vary substantially (Table 1). Although these differences may be influenced by definitions of the target and reference tissues, scanning duration, and processing methods, it is unlikely that these factors explain the large ranges of published striatal BP_ND values. Differences across scanner models are likely due to the implementation of various software programs for scatter and attenuation correction during reconstruction. To help clarify the source of the variation in measurements across laboratories, we review the advantages of the RTM and SRTM in neuroreceptor imaging and the stringent requirements for their use and extend this discussion to an investigation of the influence of choice of reference region on BP_ND using ¹⁸F-fallypride.

Table 1.

Variation of Reported ¹⁸F-Fallypride BP_ND from Reference Region Methods with the Cerebellum as a Reference Region in Healthy Subjects

					BP_ND
Study	PET Scanner	Reconstruction Algorithm	n	Age (yr), Mean ± SD	Model	Target Region	Mean ± SD
Vernaleken et al²⁹	Siemens ECAT EXACT 922/47	Filtered backprojection	10	24.4 ± 3.9	SRTM	Putamen	24.7 ± 3.6
Kegeles et al³⁰	Siemens ECAT EXACT HR+	Filtered backprojection	22	26 ± 6	SRTM	Posterior putamen	19.7 ± 2.1
Landvogt et al³¹	Siemens ECAT EXACT	Not described^*	21	30.5 ± 5.1	SRTM	Anterior putamen	10.4 ± 1.7
Cropley et al³²	GE Advance tomograph	Filtered backprojection	14	29 ± 8	SRTM	Putamen	19.8 ± 1.3
Siessmeier et al³³	Siemens ECAT EXACT	Not described^*	10	32.0 ± 9.5	SRTM	Striatum	23.3 ± 4.9
Riccardi et al³⁴	GE Discovery LS PET scanner	Not described^*	6	23–38^†	RTM³	Putamen	39.7 ± 6.1
Rominger et al³⁵	Siemens ECAT EXACT HR+	Filtered backprojection	14	44.0 ± 11.6	Logan et al³⁶	Striatum	16.8 ± 3.0
Mukherjee et al³⁷	Siemens ECAT EXACT HR+	ECAT v7.2 OSEM	6	21–63^†	Logan et al³⁶	Putamen	27.2 ± 6.1
Present data	Siemens ECAT EXACT HR+	ECAT v7.3 OSEM	43	36.1 ± 8.4	SRTM	Striatum	18.8 ± 3.8
Unpublished data^‡	Philips GEMINI TF	LOR-RAMLA	13	29.8 ± 9.0	SRTM	Striatum	29.0 ± 5.0

BP_ND = binding potential relative to nondisplaceable uptake; PET = positron emission tomography; RTM = reference tissue model; SRTM = simplified reference tissue model.

The method of reconstruction algorithm was not descried in the article.

†

Age range.

‡

Another data set, which is different from the present data set and unpublished, in our group.

The RTM and SRTM substitute an input function derived from measured activity in a reference tissue for an arterial plasma input function, obviating the need for arterial cannulation and metabolite assay. The reference tissue measurement also is preferred to the arterial measurement because it is continuous, creating reference and target TACs with equal numbers of data points. Furthermore, assuming that all radioactivity in the brain represents the parent compound,^6,7 the reference tissue measurement obviates the need for assay of radiolabeled peripheral metabolites and tracer bound to plasma protein, which contribute to uncertainty and bias in the arterial input function measurement. The requirements of RTM and SRTM are as follows:

The distribution volumes of nondisplaceable radioligand (V_ND) for the target and reference regions must be equal. Specifically, the condition V_ND = K₁/k₂ = K₁′/k₂′ must be satisfied, where the unprimed coefficients are the target tissue rate parameters and the primed coefficients are those for the reference tissue. Notably, studies with ¹⁸F-fallypride and ¹¹C-FLB 457 have shown violation of this requirement when the reference region is the cerebellum.⁸

The kinetics in the target region are such that the specifically bound and the free/nonspecific compartments are nearly indistinguishable. Accordingly, the target tissue compartments must be “well mixed” and act as a single-tissue compartment; violations of this assumption with ¹¹C-WAY-100635 produce biased BP_ND estimates from the SRTM.^9,10

The concentration of bound ligand in the reference tissue must be small compared to that of nondisplaceable ligand. This requirement is particularly important for high-affinity tracers because the ratio of bound to free ligand is ≈ k₃/k₄ = B′_max/K_D. Thus, when K_D (dissociation equilibrium constant) is small (ie, high-affinity ligands), a relatively small B′_max (estimated in vivo receptor concentration) (as in low–receptor density areas) can lead to substantial contamination of the reference tissue activity by specifically bound ligand. Studies using ¹¹C-FLB 457 have shown that specific binding contributes to activity in the cerebellum and can lead to gross underestimation (by more than a factor 2) of striatal BP_ND.^11,12

Several of the aforementioned requirements are of particular concern for the high-affinity dopamine D₂/D₃ receptor (D₂/₃R) radioligands ¹⁸F-fallypride and ¹¹C-FLB 457. Both tracers have relatively slow kinetics, requiring prolonged scanning time (2 to 3 hours' scan duration).^13,14 Due to rapid cerebellar clearance, the measured reference tissue activity is small during much of the scan. The striatal activity reaches ≈ 1 μCi/mL (decay-corrected to injection time) and remains relatively constant. The cerebellar activity (decay-corrected to injection time) falls to 0.01 to 0.02 (1–2%) of striatal activity by the end of the scan, to an absolute activity of < 10 nCi/cc, which is comparable in magnitude to activity due to scatter from the high-activity striatum.

The accuracy of BP_ND estimations using the RTM and SRTM crucially depends on how well the reference tissue TAC is measured. The reference tissue TAC is substantially smaller than the target tissue TAC during most of the scanning period, thereby having potentially large effects on the accuracy of the BP_ND estimation. Furthermore, because reference tissue activity is small, it is more vulnerable to bias produced by inaccuracies in random, scatter, or attenuation correction. Even accurate random and scatter correction degrades the statistical precision of a low-activity measurement, that is, if the corrected activity for a region is given by A_C = A_M − A_R − A_S, where A_C is corrected activity, A_M is measured activity, A_R is activity due to random coincidences, and A_S is activity due to scatter. The corresponding variance is given by V_C = V_M + V_R + V_S. Thus, precision of the reference tissue TAC may be compromised when the total measured activity is small and consists substantially of activity from scatters and randoms. Finally, another source of measurement degradation in the reference tissue TAC is reconstruction inaccuracy. Reconstruction errors occur relatively frequently in low-activity regions.^15–17 When filtered backprojection reconstruction is used, streak artifacts are particularly prominent in low-activity regions, and when iterative reconstruction is used, inaccuracy is greatest in low-activity regions because of the positivity constraint.^16,17

Although some of these problems for reference tissue methods, such as violations of the SRTM requirements, are specific for the tracer, others, such as contamination of the reference tissue activity by scatter and reconstruction artifacts, can vary among different possible reference tissues. Tissues further from high-activity regions, or from the radioactive blood pool, have less scatter contamination than closer regions. Reconstruction artifact varies across the field of view (FOV) of the scanner because scatter and attenuation are better modeled in some parts of the brain than others.¹⁵ The most substantial bias in reconstruction is found in lower brain regions, including the brainstem and cerebellum.¹⁵ Brain regions other than the cerebellum also have comparably small D₂/₃R densities^12,18 and may serve as alternative choices of a reference region with fewer of the aforementioned problems in measurement error. Therefore, we sought to determine whether, on the basis of the statistical properties of the results, we could identify one or more reference tissues as superior to others. If these reference regions have comparable tracer kinetic properties but one offers advantages in susceptibility to bias by random, scatter, or attenuation correction, then it is reasonable to consider its use as a reference tissue for PET studies with high-affinity D₂/₃R ligands. We tested the influence of the choice of reference region on striatal BP_ND using ¹⁸F-fallypride, comparing the visual cortex and a white matter region (superior longitudinal fasciculus) to the cerebellum as reference regions for striatal BP_ND measurements with ¹⁸F-fallypride, a high-affinity D₂/₃R ligand (K_D ≈ 0.20 nM in vivo).¹⁰

Materials and Methods

Research Participants

Data used in this investigation were collected in a study of the effects of methamphetamine dependence on D₂/₃R binding potential. Thirty-six methamphetamine-dependent (mean age = 34.2 years, SD = 9.5 years) and 43 age- and sex-matched control research participants (mean age = 36.1 years, SD = 8.4 years) provided written informed consent and underwent PET scanning as approved by the UCLA Institutional Review Board. Some were rescanned ≈ 30 days later. Data from controls who were retested (n = 16) were used to evaluate measurement reproducibility. A report on the effects of methamphetamine was published.¹⁹

PET Scanning

PET scanning was performed on an ECAT EXACT HR+ scanner (Siemens, Washington, DC) (measured according to National Electrical Manufacturers Association [NEMA] protocol: in-plane resolution full width at half maximum [FWHM] = 4.6 mm, axial FWHM = 3.5 mm, axial FOV = 15.52 cm) in three-dimensional [3D] mode.²⁰ A 7-minute transmission scan was performed using a rotating ⁶⁸Ge/⁶⁸Ga rod source. Dynamic data acquisition was initiated with a bolus injection of ¹⁸F-fallypride^19,21,22 (≈ 185 MBq ± 5%, specific activity ≥ 37 GBq/μmol) and continued for 80 minutes. After a short break, another transmission scan was performed, and emission data collection continued for another 80 minutes. Data were reconstructed using ECAT v7.3 ordered subset expectation maximization (OSEM) (3 iterations, 16 subsets) after corrections for decay, attenuation (segmented), and scatter.^20,23

Magnetic Resonance Imaging and Definition of Volumes of Interest

Magnetic resonance images (MRIs) were acquired at 1.5 T (Sonata, Siemens) (sagittal T₁-weighted 3D volumetric, magnetization-prepared rapid acquisition with gradient echo sequence [repetition time/echo time = 25/11 ms, number of excitations = 1, slice thickness = 1.2 mm contiguous, in-plane resolution = 1 × 1 mm², runtime = 10 minutes]) and processed using the FMRIB Software Library (FSL; Oxford University). The striatum, cerebellum, visual cortex, and white matter were defined as volumes of interest (VOI) (Figure 1). A whole-striatum VOI was created by combining bilateral caudate, putamen, and nucleus accumbens VOI, anatomically defined in native space using FSL FIRST. A cerebellum VOI was created by transforming a bilateral VOI drawn manually sampling the cerebellar hemispheres and avoiding the vermis, in MNI152 space, to native space. For white matter and visual cortex VOI, gray and white matter were first segmented using FSL FAST. Bilateral VOI sampling the visual cortex (intracalcarine cortex) and white matter (superior longitudinal fasciculus) were selected from the Harvard-Oxford cortical structural atlas and Johns Hopkins University Diffusion Tensor Imaging based white matter atlas, respectively (included in the FSL). Bilateral VOI were combined and transformed from MNI152 space into native space and were masked with the native gray matter and white matter segmentation masks, respectively, in native space (Figure 1, B–F).

Figure 1.

An example of volumes of interest (VOI) placed on the whole striatum (white), cerebellum (yellow), visual cortex (intracalcarine cortex; green), and white matter (superior longitudinal fasciculus; red) within the left hemisphere is displayed in four coronal sections (A, B, C, and D) and two sagittal sections (E and F), where a representative PET image is superimposed on the corresponding structural magnetic resonance image. The VOI volumes in this example are 20,031 mm³, 5,692 mm³, 6,986 mm³, and 5,476 mm³ for the whole striatum, cerebellum, visual cortex, and white matter, respectively. A = anterior; L = left; P = posterior; PET = positron emission tomography; R = right.

PET Image Processing

Reconstructed PET data (160 minutes) were combined into 16 images, each representing an average of 10 minutes of dynamic data. After motion correction using FSL FLIRT (six-parameter rigid-body transformation), the PET images were coregistered to the corresponding structural MRI data with the ART software package (Orangeburg, NY).²⁴ Time-radioactivity data were extracted from the VOI and imported into PMOD Kinetic Modeling Tool version 3.1 (PMOD Technologies Ltd., Zurich, Switzerland). BP_ND of ¹⁸F-fallypride to striatal D₂/₃Rs was calculated using the SRTM with each of the three reference tissues using procedures described previously.⁴

Data Analysis and Statistical Analysis

Correlation of BP_ND values calculated using the cerebellum with those using the other reference regions was tested by linear regression analysis with the Pearson correlation test. The coefficient of variation (CV = standard deviation/mean) was used as an index to assess BP_ND variability, and the Levene test was used to assess the equality of variance between the three different samples. Differences between subject groups and between scan sessions were tested by independent and paired t-tests, respectively. The effect size (Cohen d) of the between-group difference in whole-striatum BP_ND was calculated by dividing the difference in means by the standard deviation.

Test–retest variability was calculated as the ratio of the absolute value of the difference in BP_ND between the test and retest values to the mean of the test and retest BP_ND values, and the difference in variability among the three reference regions was tested by one-way repeated-measures analysis of variance (ANOVA). The results were deemed statistically significant at p < .05.

Results

The TACs obtained from each of the three reference regions exhibited similar curve shapes, especially near the end of the scan session (Figure 2). Notably, the peak activity for the white matter region was lower than those of the other regions. Regardless of the reference region used, BP_ND values obtained in this study were within the range of published striatal BP_ND values using ¹⁸F-fallypride (see Table 1). In healthy controls, BP_ND values determined with white matter as the reference region were 6.2% higher than BP_ND values determined with the cerebellum; BP_ND values determined with visual cortex were 19.9% lower than with cerebellum. In methamphetamine-dependent individuals, BP_ND values determined with white matter as the reference region were 7.7% higher than BP_ND values calculated with the cerebellum; BP_ND values determined with the visual cortex were 18.1% lower than BP_ND values determined with the cerebellum (Table 2). BP_ND values calculated using the cerebellum as the reference region were highly correlated with values obtained using white matter (r = .854, p < .001) and the visual cortex (r = .858, p < .001) (Figure 3; n = 79, groups combined).

Figure 2.

The averaged regional time-radioactivity curves for ¹⁸F-fallypride in human brain in each of three reference regions: visual cortex (intracalcarine cortex), white matter (superior longitudinal fasiculus), and cerebellum. Error bars: ± 1 SEM.

Figure 3.

Correlations of striatal BP_ND calculated using the cerebellum with BP_ND calculated using white matter (filled circles: r = .854, p < .001, y = 0.72x + 6.2) or using visual cortex (open circles: r = .858, p < .001, y = 0.60x + 3.7) in all 79 (43 control and 36 methamphetamine-dependent) participants.

Table 2.

BP_ND Values Calculated Using Each of Three Reference Regions in Healthy and Methamphetamine-Dependent Participants

	Reference Region
Participants	Cerebellum	White Matter	Visual Cortex
HC (n = 43)
Striatal BP_ND
Mean (SD)	18.85 (3.78)	20.02 (3.29)	15.09 (2.76)
CV	0.201	0.165	0.184
MA (n = 36)
Striatal BP_ND
Mean (SD)	16.75 (3.25)	18.04 (2.53)	13.71 (2.13)
CV	0.194	0.140	0.156
HC vs MA
t-test^*
t value	2.61	2.94	2.43
p value	.010	.004	.017
Effect size^†	0.60	0.68	0.57

BP_ND = binding potential relative to nondisplaceable uptake; CV = coefficient of variation = standard deviation/mean; HC = healthy participants; MA = methamphetamine-dependent participants.

Independent t-test of group difference in mean.

†

Effect size = difference in mean/standard deviation.

CV values obtained for BP_ND using white matter were 17.9%, and 27.8% lower than those calculated with the cerebellum in healthy control and methamphetamine-dependent subjects, respectively (see Table 2). CV values for BP_ND using the visual cortex were 8.4%, and 19.5%lower than those calculated with the cerebellum in healthy control and methamphetamine-dependent subjects, respectively. The Levene test revealed an inequality of variance among the three samples (Levene statistic = 4.85; p = .009, n = 79, groups combined); post hoc tests revealed greater sample variance of BP_ND using the visual cortex than the cerebellum (p = .002, n = 79 groups combined) and a trend toward inequality of variance between BP_ND using the cerebellum and white matter, with the cerebellum method showing slightly more sample variance (p = .116, n = 79, groups combined). BP_ND values obtained with each of the three reference regions showed a significant subject-group difference between healthy and methamphetamine-dependent participants by independent t-tests: white matter (p = .004), visual cortex (p = .017), and cerebellum (p = .010). The effect size of the group difference in BP_ND was largest with white matter (0.68), followed by the cerebellum (0.60) and visual cortex (0.57) (see Table 2).

To interpret the practical impact of the differences in observed effect size, we calculated the projected number of subjects needed per group (in an independent sample) to allow detection of this group difference in BP_ND with 80% power, using the observed effect sizes. The estimated number of subjects needed per group was 45 when using the cerebellum as the reference region, 50 when using the visual cortex, and 36 when using white matter.

In the test–retest subsample analysis, test–retest reproducibility among the three reference regions was similar as the BP_ND values were not significantly different between sessions, irrespective of reference region (paired t-test). Finally, test–retest variability in BP_ND using the visual cortex was 12.8% lower than variability in BP_ND using cerebellum, and variability using white matter was similar to that in cerebellum (1% lower in white matter), but there was no significant difference in variability among BP_ND data obtained with the three reference regions (one-way repeated measures ANOVA; p = .576) (Table 3).

Table 3.

Test–Retest Reproducibility in 16 Healthy Participants (n = 16)

	Reference Region
Scan Session	Cerebellum	White Matter	Visual Cortex
Test
Striatal BP_ND
Mean	19.63	20.38	15.06
CV	0.205	0.176	0.201
Retest
Striatal BP_ND
Mean	18.86	19.45	14.63
CV	0.216	0.149	0.187
Test vs retest
t-test^*
t value	1.37	1.42	1.06
p value	.19	.17	.30
VAR (%)^†
Mean ± SD	9.93 ± 9.32	9.83 ± 11.88	8.65 ± 8.59

BP_ND = binding potential relative to nondisplaceable uptake; CV = coefficient of variation = standard deviation (SD)/mean; VAR = variability.

Paired t–test.

†

VAR (%); test–retest variability (%) = 100 × |scan 2 − scan 1|/(scan 2 + scan 1)/2.

Discussion

The primary objective of this study was to investigate the influence of reference region choice on BP_ND using ¹⁸F-fallypride and the SRTM. We compared the visual cortex and the superior longitudinal fasciculus (a white matter region) to the cerebellum to determine if any region offered statistical or technical advantages over others. Using white matter as a reference region produced higher striatal BP_ND values compared to the other reference regions tested, consistent with reports of lower specific binding to D₂/₃Rs in white matter than in cerebellum or visual cortex.^12,18 In this respect, white matter adheres more to the STRM requirements of very low specific binding in the reference region than the other reference regions examined in this study. Notably, using white matter yielded the most robust group difference (largest effect size) in BP_ND between healthy and methamphetamine-dependent participants, consistent with previous reports of these differences.^19,25

Whereas other groups have noted that data obtained using ¹¹C-FLB 457 and reference regions composed mostly of gray matter exhibit TACs with markedly different shapes than those obtained using reference regions composed mostly of white matter,¹² ¹⁸F-fallypride TACs had similar shapes for all three reference tissues tested (see Figure 2), which suggests that the tracer kinetics are similar for ¹⁸F-fallypride among the reference regions examined. Higher BP_ND calculated using white matter might be explained by the lower volume of distribution (K₁′/k₂′) of white matter compared to the target tissue. Although we are not aware of studies reporting K₁′/k₂′ of white matter using ¹⁸F-fallypride, the calculated total volume of distribution for ¹¹C-FLB 457 in the cerebellum is highly correlated with that calculated in white matter.¹² However, it should be noted that values of K₁′/k₂′ obtained for D₂/₃R radioligands are both region and tracer specific.^8,26–28

The present findings suggest that using the visual cortex as a reference region with the SRTM produces BP_ND values for ¹⁸F-fallypride with higher sample variance than when the cerebellum is used. More sample variance in these data, compared to those obtained with white matter or cerebellum, may be attributed to the poorer quality of the reference region TAC measurement, possibly as a result of greater error in attenuation and scatter correction in the visual cortex when compared to the other reference regions. Attenuation and scatter correction are the most important correction processes of PET for quantitative analysis. These corrections, however, can be biased due to incorrect estimation of the attenuation coefficient. Because of poor counting statistics, bones near the lower parts of the skull are often misclassified as tissue on attenuation maps, and bias due to attenuation error in these regions is prominent. In particular, the largest negative bias from attenuation correction has occurred in the brainstem and cerebellum, whereas the largest positive bias occurred in the occipital cortex and superior portion of the cerebellum.¹⁵

On the basis of the data presented here, the visual cortex appears to offer few statistical or technical advantages over the cerebellum as a reference region for assessment of BP_ND for ¹⁸F-fallypride. However, our data support the use of the superior longitudinal fasciculus, a white matter region, as a reference region in ¹⁸F-fallypride PET studies. White matter appears comparable in performance to the cerebellum as a reference region in studies using ¹⁸F-fallypride and may offer slight statistical and technical advantages in studies using high-affinity D₂/₃R radioligands. These findings have implications that could be extended to other high-affinity radiotracers with rapid cerebellar clearance and slow kinetics.

Although this work does not fully address the limitations of reference tissue models with high-affinity radioligands, investigating the effect of reference region choice on BP_ND is an initial step toward identifying potential problems associated with modeling the kinetics of newly developed high-affinity ligands. Further investigations, including D₂/₃R-specific blocking studies, are advisable to assess the degree to which reference region methods and other kinetic modeling techniques can be used accurately with high-affinity D₂/₃R ligands to estimate binding potential. As new radiotracers are developed for use with PET, it is necessary to reevaluate the methods used to describe their binding kinetics accurately in vivo and to identify potential sources of error.

Footnotes

Acknowledgments

We are grateful to Drs. Gerhard Hellemann and J. David Jentsch for helpful comments.

Financial disclosure of authors: This research was supported by US National Institutes of Health (NIH) grants P20 DA022539, R01 DA015179, R01 DA020726 (to E.D.L.); M01 RR00865 (University of California-Los Angeles General Clinical Research Center); and endowments from the Marjorie Green Trust and the Thomas P. and Katherine K. Pike Chair in Addiction Studies (to E.D.L.). C.L.R. was supported by NIH grant T32 DA024635.

Financial disclosure of reviewers: None reported.

References

Cunningham

Jones

. Spectral analysis of dynamic PET studies. J Cereb Blood Flow Metab 1993;13:15–23, doi:10.1038/jcbfm.1993.5.

Hume

Myers

Bloomfield

. Quantitation of carbon-11-labeled raclopride in rat striatum using positron emission tomography. Synapse 1992;12:47–54, doi:10.1002/syn.890120106.

Lammertsma

Bench

Hume

. Comparison of methods for analysis of clinical [11C]raclopride studies. J Cereb Blood Flow Metab 1996;16:42–52, doi:10.1097/00004647-199601000-00005.

Lammertsma

Hume

. Simplified reference tissue model for PET receptor studies. Neuroimage 1996;4(3 Pt 1):153–8, doi:10.1006/nimg.1996.0066.

Slifstein

Laruelle

. Effects of statistical noise on graphic analysis of PET neuroreceptor studies. J Nucl Med 2000;41:2083–8.

Osman

Lundkvist

Pike

. Characterisation of the appearance of radioactive metabolites in monkey and human plasma from the 5-HT1A receptor radioligand, [carbonyl-11C]WAY-100635—explanation of high signal contrast in PET and an aid to biomathematical modelling. Nucl Med Biol 1998;25:215–23, doi:10.1016/S0969-8051(97)00206-0.

Mukherjee

Yang

Das

Brown

. Fluorinated benzamide neuroleptics—III. Development of (S)-N-[(1-allyl-2-pyrrolidinyl)-methyl]-5-(3-[18F]fluoropropyl)-2, 3-dimethoxybenzamide as an improved dopamine D-2 receptor tracer. Nucl Med Biol 1995;22:283–96, doi:10.1016/0969-8051(94)00117-3.

Christian

Narayanan

Shi

. Measuring the in vivo binding parameters of [18F]-fallypride in monkeys using a PET multiple-injection protocol. J Cereb Blood Flow Metab 2004;24:309–22, doi:10.1097/01.WCB.0000105020.93708.DD.

Oikonen

Allonen

Nagren

. Quantification of [carbonyl-(11)C]WAY-100635 binding: considerations on the cerebellum. Nucl Med Biol 2000;27:483–6, doi:10.1016/S0969-8051(00)00116-5.

10.

Slifstein

Hwang

Huang

. In vivo affinity of [18F]fallypride for striatal and extrastriatal dopamine D2 receptors in nonhuman primates. Psychopharmacology (Berl) 2004;175:274–86, doi:10.1007/s00213-004-1830-x.

11.

Asselin

Montgomery

Grasby

Hume

. Quantification of PET studies with the very high-affinity dopamine D2/D3 receptor ligand [11C]FLB 457: re-evaluation of the validity of using a cerebellar reference region. J Cereb Blood Flow Metab 2007;27:378–92, doi:10.1038/sj.jcbfm.9600340.

12.

Narendran

Mason

Chen

. Evaluation of dopamine D(2)/(3) specific binding in the cerebellum for the positron emission tomography radiotracer [(1)(1)C]FLB 457: implications for measuring cortical dopamine release. Synapse 2011;65:991–7, doi:10.1002/syn.20926.

13.

Vandehey

Moirano

Converse

. High-affinity dopamine D2/D3 PET radioligands 18F-fallypride and 11C-FLB457: a comparison of kinetics in extrastriatal regions using a multiple-injection protocol. J Cereb Blood Flow Metab 2010;30:994–1007, doi:10.1038/jcbfm.2009.270.

14.

Vernaleken

Peters

Raptis

. The applicability of SRTM in [(18)F]fallypride PET investigations: impact of scan durations. J Cereb Blood Flow Metab 2011;31:1958–66, doi:10.1038/jcbfm.2011.73.

15.

Son

Kim

. Analysis of biased PET images caused by inaccurate attenuation coefficients. J Nucl Med 2010;51:753–60, doi:10.2967/jnumed.109.070326.

16.

Belanger

Mann

Parsey

. OS-EM and FBP reconstructions at low count rates: effect on 3D PET studies of [11C] WAY-100635. Neuroimage 2004;21:244–50, doi:10.1016/j.neuroimage.2003.08.035.

17.

Reilhac

Tomei

Buvat

. Simulation-based evaluation of OSEM iterative reconstruction methods in dynamic brain PET studies. Neuroimage 2008;39:359–68, doi:10.1016/j.neuroimage.2007.07.038.

18.

Hall

Farde

Halldin

. Autoradiographic localization of extrastriatal D2-dopamine receptors in the human brain using [125I]epidepride. Synapse 1996;23:115–23, doi:10.1002/(SICI)1098-2396(199606)23:2<115::AID-SYN7>3.0.CO;2-C.

19.

Lee

London

Poldrack

. Striatal dopamine D2/D3 receptor availability is reduced in methamphetamine dependence and is linked to impulsivity. J Neurosci 2009;29:14734–40, doi: 10.1523/JNEUROSCI.3765-09.2009.

20.

Brix

Zaers

Adam

. Performance evaluation of a whole-body PET scanner using the NEMA protocol. National Electrical Manufacturers Association. J Nucl Med 1997;38:1614–23.

21.

Brown

Mandelkern

Farahi

. Sex differences in striatal dopamine D2/D3 receptor availability in smokers and non-smokers. Int J Neuropsychopharmacol 2012;15:989–94.

22.

Ishibashi

Berman

Paz-Filho

. Dopamine D2/D3 receptor availability in genetically leptin-deficient patients after long-term leptin replacement. Mol Psychiatry 2012;17:352–3, doi: 10.1038/mp.2011.156.

23.

Watson

. New, faster, image-based scatter correction for 3D PET. IEEE Trans Nucl Sci 2000;47:1587–94, doi:10.1109/23.873020.

24.

Ardekani

Braun

Hutton

. A fully automatic multimodality image registration algorithm. J Comput Assist Tomogr 1995;19:615–23, doi:10.1097/00004728-199507000-00022.

25.

Volkow

Chang

Wang

. Low level of brain dopamine D2 receptors in methamphetamine abusers: association with metabolism in the orbitofrontal cortex. Am J Psychiatry 2001;158:2015–21, doi:10.1176/appi.ajp.158.12.2015.

26.

Olsson

Halldin

Swahn

Farde

. Quantification of [11C]FLB 457 binding to extrastriatal dopamine receptors in the human brain. J Cereb Blood Flow Metab 1999;19:1164–73, doi:10.1097/00004647-199910000-00013.

27.

Ito

Sudo

Suhara

. Error analysis for quantification of [(11)C]FLB 457 binding to extrastriatal D(2) dopamine receptors in the human brain. Neuroimage 2001;13:531–9, doi:10.1006/nimg.2000.0717.

28.

Ginovart

Willeit

Rusjan

. Positron emission tomography quantification of [11C]-(+)-PHNO binding in the human brain. J Cereb Blood Flow Metab 2007;27:857–71.

29.

Vernaleken

Klomp

Moeller

. Vulnerability to psychotogenic effects of ketamine is associated with elevated D2/3-receptor availability. Int J Neuropsychopharmacol 2013;16:745–54.

30.

Kegeles

Slifstein

. Striatal and extrastriatal dopamine D2/D3 receptors in schizophrenia evaluated with [18F]fallypride positron emission tomography. Biol Psychiatry 2010;68:634–41, doi:10.1016/j.biopsych.2010.05.027.

31.

Landvogt

Buchholz

Bernedo

. Alteration of dopamine D2/D3 receptor binding in patients with juvenile myoclonic epilepsy. Epilepsia 2010;51:1699–706, doi:10.1111/j.1528-1167.2010.02569.x.

32.

Cropley

Innis

Nathan

. Small effect of dopamine release and no effect of dopamine depletion on [18F]fallypride binding in healthy humans. Synapse 2008;62:399–408, doi:10.1002/syn.20506.

33.

Siessmeier

Zhou

Buchholz

. Parametric mapping of binding in human brain of D2 receptor ligands of different affinities. J Nucl Med 2005;46:964–72.

34.

Riccardi

Baldwin

Salomon

. Estimation of baseline dopamine D2 receptor occupancy in striatum and extrastriatal regions in humans with positron emission tomography with [18F] fallypride. Biol Psychiatry 2008;63:241–4, doi:10.1016/j.biopsych.2007.03.022.

35.

Rominger

Cumming

Xiong

. [18F]Fallypride PET measurement of striatal and extrastriatal dopamine D 2/3 receptor availability in recently abstinent alcoholics. Addict Biol 2011;17:490–503, doi:10.1111/j.1369-1600.2011.00355.x.

36.

Logan

Fowler

Volkow

. Distribution volume ratios without blood sampling from graphical analysis of PET data. J Cereb Blood Flow Metab 1996;16:834–40, doi:10.1097/00004647-199609000-00008.

37.

Mukherjee

Christian

Dunigan

. Brain imaging of 18F-fallypride in normal volunteers: blood analysis, distribution, test-retest studies, and preliminary assessment of sensitivity to aging effects on dopamine D-2/D-3 receptors. Synapse 2002;46:170–88, doi:10.1002/syn.10128.

The Simplified Reference Tissue Model with 18 F-Fallypride Positron Emission Tomography: Choice of Reference Region

Abstract

Materials and Methods

Research Participants

PET Scanning

Magnetic Resonance Imaging and Definition of Volumes of Interest

PET Image Processing

Data Analysis and Statistical Analysis

Results

Discussion

Footnotes

Acknowledgments

References