Noninvasive k_{3} estimation method for slow dissociation PET ligands: application to [^{11}C]Pittsburgh compound B
 Koichi Sato^{1, 2},
 Kiyoshi Fukushi^{1},
 Hitoshi Shinotoh^{1, 3},
 Hitoshi Shimada^{1, 4},
 Shigeki Hirano^{1, 5},
 Noriko Tanaka^{6},
 Tetsuya Suhara^{1},
 Toshiaki Irie^{1} and
 Hiroshi Ito^{1}Email author
DOI: 10.1186/2191219X376
© Sato et al.; licensee Springer. 2013
Received: 4 October 2013
Accepted: 6 November 2013
Published: 16 November 2013
Abstract
Background
Recently, we reported an information density theory and an analysis of threeparameter plus shorter scan than conventional method (3P+) for the amyloidbinding ligand [^{11}C]Pittsburgh compound B (PIB) as an example of a nonhighly reversible positron emission tomography (PET) ligand. This article describes an extension of 3P + analysis to noninvasive ‘3P++’ analysis (3P + plus use of a reference tissue for input function).
Methods
In 3P++ analysis for [^{11}C]PIB, the cerebellum was used as a reference tissue (negligible specific binding). Fifteen healthy subjects (NC) and fifteen Alzheimer's disease (AD) patients participated. The k _{3} (index of receptor density) values were estimated with 40min PET data and threeparameter reference tissue model and were compared with that in 40min 3P + analysis as well as standard 90min fourparameter (4P) analysis with arterial input function. Simulation studies were performed to explain k _{3} biases observed in 3P++ analysis.
Results
Good model fits of 40min PET data were observed in both reference and target regionsofinterest (ROIs). High linear intrasubject (inter15 ROI) correlations of k _{3} between 3P++ (Yaxis) and 3P + (Xaxis) analyses were shown in one NC (r^{2} = 0.972 and slope = 0.845) and in one AD (r^{2} = 0.982, slope = 0.655), whereas intersubject k _{3} correlations in a target region (left lateral temporal cortex) from 30 subjects (15 NC + 15 AD) were somewhat lower (r^{2} = 0.739 and slope = 0.461). Similar results were shown between 3P++ and 4P analyses: r^{2} = 0.953 for intrasubject k _{3} in NC, r^{2} = 0.907 for that in AD and r^{2} = 0.711 for inter30 subject k _{3}. Simulation studies showed that such lower intersubject k _{3} correlations and significant negative k _{3} biases were not due to unstableness of 3P++ analysis but rather to intersubject variation of both k _{2} (index of braintoblood transport) and k _{3} (not completely negligible) in the reference region.
Conclusions
In [^{11}C]PIB, the applicability of 3P++ analysis may be restricted to intrasubject comparison such as followup studies. The 3P++ method itself is thought to be robust and may be more applicable to other nonhighly reversible PET ligands with ideal reference tissue.
Keywords
[^{11}C]Pittsburgh compound B Alzheimer's disease Kinetic modeling PET quantification Reference tissue Slow dissociation ligandBackground
Various reversibletype radioligands have been developed for in vivo neuroreceptor study with positron emission tomography (PET). Both arterial blood sampling and long dynamic PET scan, up to 120 min, are required for standard nonlinear leastsquares (NLS) analysis to estimate K _{1} to k _{4} in the twotissue compartment fourparameter model (4P model): K _{1} represents the bloodtobrain transport constant, k _{2} represents the braintoblood transport constant, k _{3} represents the firstorder association rate constant for specific binding, and k _{4} represents the dissociation rate constant for specific binding. The k _{3} represents B _{max}·k _{on}, where B _{max} is maximum receptor density and k _{on} is the in vivo association rate constant. Since k _{3} represents available receptors for the PET ligand, it is the target parameter of major interest in most PET studies. However, quantification of k _{3} in the 4P model is often difficult because of uncertainty of the k _{4} estimate and high correlation between the k _{3} and k _{4} estimates. As surrogate parameters for B _{max}, binding potential and distribution volume have been widely used [1–4]. Several reference tissue methods have also been developed [5–10].
Irreversible (enzymesubstrate type) radiotracers [^{11}C]methylpiperidin4yl acetate and propionate have been developed for the measurement of cerebral acetylcholine esterase activity using PET [11, 12]. In this case the twotissue compartment threeparameter (K _{1} to k _{3}) model (3P model) was used to estimate k _{3}, which is an index of acetylcholine esterase activity. In the 3P model, the precision of k _{3} estimate is usually higher than in the 4P model, in spite of shorter PET scan time (40 to 60 min), since there is no need of k _{4} estimation in the 3P model.
We have previously defined two mathematical functions, the information density function and information function, which are useful for model selection and optimization of scan time in PET [13]. Based on simulations using both functions, we proposed a new method (3P + method) for quantification of k _{3} for moderately reversible ligands. ‘3P+’ means threeparameter model plus short PET scan. In this method, the 3P model (k _{4} = 0 model) was applied to the earlyphase PET data (up to 30 to 40 min) from reversible ligands with moderate k _{4} (moderately reversible ligands). Although the 3P + method was not always developed for a specific ligand, the amyloidbinding radiotracer [^{11}C]Pittsburgh compound B (PIB) was used as an example for the moderately reversible ligands (k _{4} = 0.018/min). The 3P + method afforded a more stable k _{3} estimate than the standard 90min 4P analysis. However, there is still the drawback of the necessity for arterial blood sampling and radiometabolite analysis, which may restrict the widespread use of this method in daily clinical practice.
In this article, we propose a noninvasive 3P++ analysis using [^{11}C]PIB. 3P++ means 3P + analysis plus use of a reference tissue for input function. To validate the proposed method, the linear correlations of k _{3} estimates were evaluated between 40min 3P++ and 3P + analyses, as well as between 3P++ and 90min 4P analyses in clinical PET studies. In addition, simulation studies were performed to explain k _{3} biases observed in the 3P++ analysis.
Methods
Theory
Assumptions in 3P++ analysis
The following are assumptions used in 3P++ analysis:

Assumption 1 (on the nature of radioligand used): We apply 3P++ analysis only to moderately reversible or nearly irreversible radioligands (k _{4} ≤ 0.03/min), but exclude highly reversible ligands. [^{11}C]PIB is an example of moderately reversible ligands (k _{4} = 0.018/min).

Assumption 2 (on the duration time of PET scan): We use earlyphase PET data in the curve fitting. In [^{11}C]PIB, dynamic PET data during 0 to 40 min was described well with the 3P model, since the effect of the k _{4} process on PET data was negligible within these earlyphase kinetics [13].

Assumption 3 (on the specific binding in the reference tissue, k _{3r}): Specific binding of radioligand is negligible in the reference tissue (k _{3r} = 0). In [^{11}C]PIB, the gray matter of the cerebellum is usually used as a reference tissue for input function [14]. We apply the onetissue compartment twoparameter (K _{1}, k _{2}) model (2P model) to the reference tissue.
Working equation for 3P++ analysis
where C _{ t }(t) is the radioactivity concentration in the target tissue and C _{ r }(t) is that in the reference tissue; k _{2r} is the k _{2} in the reference tissue and ⊗ is the convolution integral. The rate of tracer penetration into the target tissue is obtained as the relative value R _{1}, which is the ratio of target K _{1} to reference K _{1}.
Clinical PET study
Human subjects
Two groups of subjects, a normal control (NC) group and an Alzheimer's disease (AD) group, participated in the current study with written informed consent. The NC group consisted of 15 healthy subjects (age ranging from 48 to 90 years, 66.7 ± 11.5 years (mean ± SD); eight males and seven females) without a history of central nervous system diseases or psychiatric disorders, and the AD group consisted of 15 patients (ages 55 to 85, 68.9 ± 9.6 years; four males and 11 females) diagnosed as probable AD according to the criteria of the National Institute of Neurological and Communication Disorders, Alzheimer's Disease and Related Disorders Association [16]. The study was approved by the Institutional Review Board of the National Institute of Radiological Sciences.
Radiochemical synthesis
[^{11}C]PIB was synthesized by the reaction of 2(4′aminophenyl)6hydroxybenzothiazole and [^{11}C]methyl triflate [17]. The product had radiochemical purity greater than 95.4%. Specific activity was in the range of 56.3 to 285.3 GBq/μmol.
PET scan protocol
PET images were acquired with a Siemens ECAT EXACT HR + scanner (CTI PET systems, Inc., Knoxville, TN, USA) with an axial field of view of 155 mm, providing 63 contiguous 2.46mm slices with 5.6mm transaxial and 5.4mm axial resolution. After a 10min transmission scan for tissue attenuation correction, infusion of [^{11}C]PIB (about 370 MBq in 5 mL for 1 min) began. A PET scan in 3D mode was started after the arrival of tracer to the brain (approximately 30 s after the beginning of tracer infusion). The dynamic scans consisted of 19 frames (3 × 20 s, 3 × 40 s, 1 × 1 min, 2 × 3 min, 5 × 6 min, and 5 × 10 min) with the total scan duration of 90 min. All data processing and image reconstruction were performed using standard Siemens software, which included scatter correction, randoms, and dead time correction.
Regionofinterest delineation
Regionofinterest (ROI) analysis was performed using the PMOD software package (PMOD version 3.2; Technologies Ltd., Adliswil, Switzerland). The [^{11}C]PIB PET images were coregistered to T _{1} weighted images in each subject. The following 15 ROIs were drawn manually on T _{1} weighted images: frontal, mesial temporal, lateral temporal, parietal, occipital, anterior cingulate, and posterior cingulate cortices in both hemispheres as well as the reference tissue (gray matter of cerebellum). ROIs were transferred to coregistered [^{11}C]PIB PET images, and timeactivity curves (TACs) were obtained in those brain regions.
Input function measurement
During PET scan, arterial blood was collected from radial artery, starting 6 s (transit delay at the blood sampling site) after the beginning of PET scan to 85 min post injection (10 × 10 s, 1 × 30 s, 9 × 2 min, 6 × 10 min, and 1 × 5 min; 27 samples). Radioactive metabolites were analyzed by a radiothin layer chromatography (TLC) method [12], with a TLCdeveloping solvent (ethyl acetate/nhexane = 2:1 vols). The metabolitecorrected radioactivity as well as total radioactivity in blood plasma was fitted to a monoexponential saturation function during infusion (0 to 1 min) and the sum of threeexponential functions after the end of infusion (1 to 85 min) [12].
4P and 3P + analyses (arterialplasma input)
Brain regional TACs were analyzed by the weighted NLS method under positive constraint of all k _{ i } with metabolitecorrected input function to afford K _{1} to k _{4} estimates in 4P analysis (scan time of 90 min) and K _{1} to k _{3} estimates in 3P + analysis (40 min). Correction was made for bloodpool (5%) radioactivity in brain tissue [14]. Custom software operating in IDL software (version 6.0; Jicoux Datasystems, Inc., Tokyo, Japan) environment was used for the compartment model analysis.
3P++ analysis (reference tissue input)
For successful convergence in NLS optimization using Equation 1, we fixed k _{2r} to 0.178/min (mean cerebellar k _{2} value by 40min 3P + analysis; N = 30; SD = 0.034). Based on Equation 1 and cerebellar TAC with a fixed k _{2r} value, the timeintegral of C _{ r }(t) (the second term on the right side of Equation 1) and the convolution integral (the third term) were calculated numerically without data interpolation for each scan midtimes during 0 to 40 min, and the three parameters R _{1}, k _{2}, and k _{3} were estimated.
Simulation study
Generation of erroradded TACs for Monte Carlo simulation
Effects of PET noise on 4P, 3P+, and 3P++ analyses
Five levels of PET noise (0.025, 0.05, 0.1, 0.2, and 0.3; ϵ in Equation 2, relative values empirically determined) were added to the baseline TACs of the target ROI of the NC subjects. From 100 erroradded TACs for each PET noise level, 100 k _{3} values were estimated using 90min 4P, 40min 3P+, and 3P++ analyses. Coefficientofvariation (CV) of k _{3} was calculated as CV (%) = (SD/mean) × 100. In the following simulations, the PET noise was fixed at 0.1.
Effects of K_{1} change in target ROI on 4P, 3P+, and 3P++ analyses
Simulated target TACs were generated by 4P model with five different K _{1} values (0.12, 0.15, 0.18, 0.21, and 0.24 mL/g/min) and fixed k _{3} (0.018/min) and k _{4} (0.018/min). The value of K _{1}/k _{2} was fixed at 1. The range of K _{1} was determined with clinically measured K _{1} for [^{11}C]PIB (0.177 ± 0.31 in NC group and 0.168 ± 0.30 in AD group; 90min 4P analysis). Reference TAC was the same as baseline reference TAC. The k _{3} bias in 90min 4P, 40min 3P+, and 3P++ analyses relative to the true k _{3} (0.018/min) was calculated as bias (%) = (estimated k _{3}/true k _{3}  1) × 100.
Effects of k_{2} or k_{3} change in reference ROI on 3P++ analysis
In 3P++ analysis, k _{3r} was assumed to be 0 and k _{2r} was fixed as an empirical constant. The effects of k _{2r} or k _{3r} change were investigated as follows. The erroradded target TACs were generated by 4P model with two different k _{3} values (0.018/min for NC and 0.036/min for AD); other parameters were the same as the baseline target TAC. The erroradded reference TACs were generated by 2P model with five different k _{2} (0.12, 0.15, 0.18, 0.21, and 0.24/min) and fixed K _{1} values (0.18 mL/g/min). Another set of simulated reference TACs was generated by 3P model (not 2P model) with five different k _{3} (0, 0.002, 0.004, 0.006, and 0.008/min) and fixed K _{1} (0.18 mL/g/min) and k _{2} (0.18/min). The k _{3} bias in 3P++ analysis was expressed relative to 3P + analysis as bias (%) = (3P++ k _{3}/3P + k _{3}  1) × 100.
Although k _{3r} was assumed to be 0 in Equation 1, each subject may have different k _{3r} values that deviated from 0. In simulations to investigate the effect of the individual k _{3r} variation on 3P++ analysis, we defined the k _{3} value empirically corrected for nonzero k _{3r} as follows: k _{3}′ = k _{3} + k _{3r}, where k _{3} is the k _{3} estimate of target ROI by 3P++ analysis and k _{3r} is the k _{3} estimate of reference ROI by 3P + analysis (true reference k _{3}). Bias in 3P++ k _{3}′ relative to 3P + k _{3} was compared with the bias in 3P++ k _{3} to 3P + k _{3}.
Results
Goodness of model fits in 3P++ analysis
Intrasubject k_{3} correlation
Figure 2B shows the k _{3} correlation between 90min 4P (Xaxis) and 40min 3P++ (Yaxis) analyses in the same subjects. The regression lines are Y = 0.590X  0.005 (r^{2} = 0.953) for the NC subject and Y = 0.338X + 0.000 (r^{2} = 0.907) for the AD subject. When the cerebellar data (X = 0.008, Y = 0.000) was removed from calculation for the AD subject, the regression line became Y = 0.295X  0.002 with slightly larger r^{2} (0.935; not shown in the figure). The slopes of the regression lines show that k _{3} bias in 3P++ against 4P analysis is larger than that against 3P + analysis.
Intersubject k_{3} correlation
Figure 3B shows the intersubject correlation of left lateral temporal k _{3} between 90min 4P (Xaxis) and 40min 3P++ (Yaxis) analyses, where the regression line is Y = 0.225X + 0.000 (r^{2} = 0.711) for all subjects. The lines of Y = 0.090X + 0.001 (r^{2} = 0.122) for the NC group alone and Y = 0.135X + 0.005 (r^{2} = 0.513) for the AD group alone were also calculated. The slopes of the regression lines show larger negative k _{3} biases in 3P++ against 4P analysis than that shown in Figure 3A. The results in other cerebral regions were essentially the same as those in the left lateral temporal cortex.
Simulation on the effects of PET noise on k_{3} CV
Simulation on the effects of target K_{1} change on k_{3} bias
Simulation on the effects of k_{2r} change on 3P++ analysis
Simulation on the effects of k_{3r} change on 3P++ analysis
In 3P++ analysis we assume that k _{3r} = 0, that is, specific binding is negligible in the reference tissue. However, in all subjects examined, this assumption did not hold: the k _{3r} values in 40min 3P + analysis were 0.008 ± 0.004/min in the AD group, 0.007 ± 0.002/min in the NC group, and 0.007 ± 0.003/min in the AD + NC group.
Discussion
Theoretical basis and merits of 3P++ analysis
The previous 3P + analysis allowed for estimating k _{3} of moderately reversible ligands, where the 3P model was applied to earlyphase (up to 30 to 40 min) PET data with arterial input function [13]. It was reported that when the 3P model was applied to 60min PET scan data from [^{11}C]PIB (k _{4} = 0.018/min) as a moderately reversible ligand, only a poor model fit was obtained [19]. Previous simulation studies on [^{11}C]PIB using information density theory suggested that scan time reduction to 40 min would be necessary to obtain a good fit to the 3P model [13].
When 3P + or 3P++ analysis can be applied to a ligand, such ligand is specified as a moderately reversible ligand. This applicability is determined by the information function curves of k _{3} and k _{4} [13], and thus is dependent on the scan time as well as k _{3} and k _{4} values of the ligand in a ROI. Differentiation of a moderately reversible ligand from general reversible ligands is somewhat arbitrary, though we conveniently defined this with the k _{4} value (≤0.03/min) in this study.
In the present study, the 3P + plasma input model was extended to the 3P++ reference tissue input model. The 3P++ analysis has three merits over previous methods. First, the PET scan time is short, usually less than 40 min, which may be important in PET studies with elderly or demented subjects. Secondly, the target parameter k _{3} can be isolated from the other model parameters. Thirdly, neither arterial cannulation nor laborintensive measurements of labeled metabolites are required.
One of the conventional models for the estimation of binding of [^{11}C]PIB is the Logan plot analysis [2], which employs data of long duration (more than 60 min). Noninvasive Logan analysis (distribution volume ratio) [6] requires latephase (equilibriumphase) PET data, whereas latephase data are not necessary for 3P++ analysis. In the noninvasive Logan model or simplified reference tissue model [8], the K _{1}tok _{2} ratio in the target and reference tissues is assumed to be equal. 3P++ analysis does not require such an assumption. Since 3P++ analysis is a kind of irreversiblemodel analysis, K _{1} (R _{1}) and k _{3} can be independently estimated (k _{2} must be fixed to a certain constant).
Noise sensitivity of 3P++ analysis
Loss of PET data in shortscan 3P++ and 3P + analyses might be considered to deteriorate the precision of the k _{3} estimate. In the present simulation for noise sensitivity, k _{3} CV values in 40min 3P++ and 3P + analyses were lower than (almost three fifths of) that in 90min 4P analysis (Figure 4), which was in accordance with the previous report [13]. It is considered that the loss of PET data may be compensated for by the reduction in the number of free parameters from four in the 4P model to three in the 3P + and 3P++ models.
K_{1} effect on 3P++ analysis
In the K _{1} simulation, the stableness of k _{3} estimation in changes of cerebral blood flow was investigated. The magnitudes of k _{3} bias were independent of the K _{1} change, ranging from 0.12 to 0.24 mL/g/min, in 3P++, 3P+, and 4P analyses (Figure 5). The 3P++ as well as 3P + and 4P analyses were less affected by K _{1}, which is owing to the capability of isolating the k _{3} estimation. The 40min 3P + analysis showed 33% k _{3} bias relative to 90min 4P analysis, which is in accordance with the previous report [13]. In this K _{1} simulation, 3P++ k _{3} showed negligible bias relative to 3P + k _{3}. These results suggested that in 3P++ analysis, the effects of ignoring vascular volume as well as numerical integration error due to discrete time points were not significant.
Causes of negative k_{3} bias in 3P++ analysis
Firstly, the k _{3} bias in 3P++ analysis originates from 3P model approximation. Our previous simulation study [13] showed that the 3P + analysis with 28min scan had large negative k _{3} bias relative to 4P analysis with 90min scan; for example, there was about 22% to 24% bias to true k _{3} (4P k _{3}) ranging from 0.01 to 0.04/min including NC and AD k _{3}. 3P++ analysis showed further negative k _{3} bias relative to 3P + analysis due to the following two reasons.
Secondly, the bias is due to individual k _{2r} change from the fixed value in Equation 1. In 3P++ analysis, we also assumed that k _{2} in the reference tissue was constant and was fixed at 0.178/min, which was the average k _{2} value with the 3P + model. In simulation, negative k _{3} bias was predicted when k _{2r} was larger or smaller than fixed k _{2} (Figure 6). Each subject in the NC and AD groups had different k _{2} values in the reference tissue, and it is considered that such biological variance as for reference tissue may result in a negative k _{3} bias in 3P++ analysis, relative to 3P + analysis for [^{11}C]PIB.
Thirdly, the bias is due to the discrepancy between the model assumption and the actual reference ROI. The basic assumption (assumption 3) in 3P++ analysis is k _{3r} = 0. The working equation of 3P++ analysis (Equation 1) is derived under this assumption, and reference k _{3} is naturally calculated to be 0. However, in 3P + analysis with [^{11}C]PIB, the cerebellum showed nonzero k _{3} (0.007 ± 0.003/min in all 30 subjects). Thus, 3P++ k _{3} is expected to be underestimated. Simulation studies showed that 3P++ analysis was biasfree for ideal reference with zero k _{3} and that k _{3} bias became larger as k _{3r} increased (Figure 7). When k _{3} was replaced by k _{3}′, negative bias was significantly decreased in the simulation (Figure 7), as well as the slope of the regression line between 3P++ and 3P + analyses being increased from 0.461 (Figure 3A) to 0.678 (Figure 8), which also suggested that nonzero k _{3r} caused underestimation of 3P++ k _{3}.
Correlation of k_{3} between 3P++ and 3P + analyses
Strong intrasubject k _{3} correlation was shown between 3P++ and 3P + analyses, and the rankorder of k _{3} was almost the same between the two analyses (Figure 2A), suggesting the stability of both 3P++ and 3P + analyses.
The intersubject k _{3} correlation (r^{2}; Figure 3A) was significantly lower than the intrasubject correlation (Figure 2A). Such a lower intersubject k _{3} correlation can be partly explained by the sample variance of cerebellar k _{3}. In order to explain this, k _{3}′ was calculated for each subject. When k _{3} was replaced by k _{3}′, the determination coefficient between 3P++ and 3P + analyses was increased from 0.739 (Figure 3A) to 0.975 (Figure 8); the latter is comparable to r^{2} of the intrasubject k _{3} correlation (0.982; Figure 2A).
Such an estimation of parameter k _{3}′ is not always practical, as 3P + analysis with arterial input function is necessary for individual cerebellar k _{3} estimation. However, these results suggest that the lower r^{2} in the intersubject correlation compared with the intrasubject correlation is due to the sample variance of cerebellar k _{3} and that 3P++ analysis itself is robust, as far as the reference is ideal.
Practically, the use of mean k _{3r} may be meaningful. When target k _{3} is empirically corrected as corrected k _{3} = estimated k _{3} + mean cerebellar k _{3}, the absolute bias in target k _{3} would decrease. However, the precision of target k _{3} would not necessarily be improved owing to the variance of individual k _{3r}.
In addition to the nonzero effect of k _{3r}, intersubject variation of k _{2r} from the fixed value (k _{2} = 0.178/min) may also produce individually different k _{3} bias in 3P++ analysis, resulting in lower intersubject k _{3} correlation between 3P + and 3P++ analyses.
Limitations of 3P++ analysis
When 3P++ analysis was applied to [^{11}C]PIB as an example of moderately reversible ligands, a somewhat lower intersubject k _{3} correlation (r^{2} = 0.739 or 0.711; Figure 3A or Figure 3B) was shown between the 3P++ and 3P + or 4P analyses, respectively, across a k _{3} range including NC and AD (3P + k _{3}, 0.004 to 0.040/min). The rank order of 3P++ k _{3} also differed considerably from 3P + k _{3} or 4P k _{3}. These results were mainly due to nonzero k _{3r} and the sample variance of both k _{2r} and k _{3r} as described above. The negative k _{3} bias (3P++ vs. 3P+) was larger in NC ROI (70%) than in AD ROI (48%) when k _{3r} = 0.008/min (Figure 7). The previous report showed that the difference in k _{3} bias (28min 3P + vs. 90min 4P) was small between NC ROI (23%) and AD ROI (24%) [13]. Therefore, the k _{3} value in 3P++ analysis may be somewhat underestimated in the ROI with lower amyloid deposition compared to 3P + or 4P analysis.
In [^{11}C]PIB PET, 3P++ analysis may be inadequate for intersubject k _{3} comparison and useful only for intrasubject (interROI) comparison or pre vs. postcomparison in the same subject. 3P++ analysis would be more suitable for such reversible ligands that have moderate k _{4} and reference tissue without specific binding.
Conclusions
The 3P++ analysis is a k _{3} estimation method for moderately reversible PET ligands with a short scan time such as 40 min and without arterial blood sampling. Although the applicability of 3P++ method to [^{11}C]PIB PET may be restricted to intrasubject comparison, 3P++ analysis itself is robust. The 3P++ method would be useful for PET study with nonhighly reversible ligands, as far as the reference tissue without specific binding is available.
Declarations
Acknowledgements
The authors thank the production team staff for the production of isotopes and the PET operation staff for the acquisition of PET images.
Authors’ Affiliations
References
 Mintun MA, Raichle ME, Kilbourn MR, Wooten GF, Welch MJ: A quantitative model for the in vivo assessment of drug binding sites with positron emission tomography. Ann Neurol 1984, 15: 217–227. 10.1002/ana.410150302View ArticlePubMedGoogle Scholar
 Logan J, Fowler JS, Volkow ND, Wolf AP, Dewey SL, Schlyer DJ, MacGregor RR, Hitzemann R, Bendriem B, Gatley SF, Christman DR: Graphical analysis of reversible radioligand binding from timeactivity measurements applied to [N^{ 11 } Cmethyl]()cocaine PET studies in human subjects. J Cereb Blood Flow Metab 1990, 10: 740–747. 10.1038/jcbfm.1990.127View ArticlePubMedGoogle Scholar
 Hume SP, Myers R, Bloomfield PM, OpackaJuffry J, Cremer JE, Ahier RG, Luthra SK, Brooks DJ, Lammertsma AA: Quantification of carbon11labeled racropride in rat striatum using positron emission tomography. Synapse 1992, 12: 47–54. 10.1002/syn.890120106View ArticlePubMedGoogle Scholar
 Innis RB, Cunningham VJ, Delforge J, Fujita M, Gjedde A, Gunn RN, Holden J, Houle S, Huang SC, Ichise M, Iida H, Ito H, Kimura Y, Koeppe RA, Knudsen GM, Knuuti J, Lammertsma AA, Laruelle M, Logan J, Maguire RP, Mintun MA, Morris ED, Parsey R, Price JC, Slifstein M, Sossi V, Suhara T, Votaw JR, Wong DF, Carson RE: Consensus nomenclature for in vivo imaging of reversibly binding radioligands. J Cereb Blood Flow Metab 2007, 27: 1533–1539. 10.1038/sj.jcbfm.9600493View ArticlePubMedGoogle Scholar
 Ichise M, Ballinger JR, Golan H, Vines D, Luong A, Tsai S, Kung HF: Noninvasive quantification of dopamine D2 receptors with iodine123IBF SPECT. J Nucl Med 1996, 37: 513–520.PubMedGoogle Scholar
 Lammertsma AA, Hume SP: Simplified reference tissue model for PET receptor studies. Neuroimage 1996, 4: 153–158. 10.1006/nimg.1996.0066View ArticlePubMedGoogle Scholar
 Lammertsma AA, Bench CJ, Hume SP, Osman S, Gunn K, Brooks DJ, Frackowiak RSJ: Comparison of methods for analysis of clinical [^{ 11 } C]raclopride studies. J Cereb Blood Flow Metab 1996, 16: 42–52.View ArticlePubMedGoogle Scholar
 Logan J, Fowler JS, Volkow ND, Wang GJ, Ding YS, Alexoff DL: Distribution volume ratios without blood sampling from graphical analysis of PET data. J Cereb Blood Flow Metab 1996, 16: 834–840.View ArticlePubMedGoogle Scholar
 Watabe H, Carson RE, Iida H: The reference tissue model: three compartments for the reference region [abstract]. Neuroimage 2000, 11: S12. 10.1016/S10538119(00)909479View ArticleGoogle Scholar
 Wu Y, Carson RE: Noise reduction in the simplified reference tissue model for neuroreceptor functional imaging. J Cereb Blood Flow Metab 2002, 22: 1440–1452.View ArticlePubMedGoogle Scholar
 Koeppe RA, Frey KA, Snyder SE, Meyer P, Kilbourn MR, Kuhl DE: Kinetic modeling of N[^{ 11 } C]methylpiperidin4yl propionate: alternatives for analysis of an irreversible positron emission tomography tracer for measurement of acetylcholinesterase activity in human brain. J Cereb Blood Flow Metab 1999, 19: 1150–1163.View ArticlePubMedGoogle Scholar
 Namba H, Iyo M, Fukushi K, Shinotoh H, Nagatsuka S, Suhara T, Sudo Y, Suzuki K, Irie T: Human cerebral acetylcholinesterase activity measured with positron emission tomography: procedure, normal values and effect of age. Eur J Nucl Med 1999, 26: 135–143. 10.1007/s002590050369View ArticlePubMedGoogle Scholar
 Sato K, Fukushi K, Shinotoh H, Shimada H, Tanaka N, Hirano S, Irie T: A shortscan method for k _{ 3 } estimation with moderately reversible PET ligands: application of irreversible model to earlyphase PET data. Neuroimage 2012, 59: 3149–3158. 10.1016/j.neuroimage.2011.10.087View ArticlePubMedGoogle Scholar
 Price JC, Klunk WE, Lopresti BJ, Lu X, Hoge JA, Ziolko SK, Holt DP, Meltzer CC, DeKosky ST, Mathis CA: Kinetic modeling of amyloid binding in humans using PET imaging and Pittsburgh CompoundB. J Cereb Blood Flow Metab 2005, 25: 1528–1547. 10.1038/sj.jcbfm.9600146View ArticlePubMedGoogle Scholar
 Gunn RN, Gunn SR, Cunningham VJ: Positron emission tomography compartmental models. J Cereb Blood Flow Metab 2001, 21: 635–652.View ArticlePubMedGoogle Scholar
 McKhann G, Drachman D, Folstein M, Katzman R, Price D, Stadlan EM: Clinical diagnosis of Alzheimer's disease: report of the NINCDSADRDA work group under the auspices of Department of Health and Human Services Task Force on Alzheimer's Disease. Neurology 1984, 34: 939–944. 10.1212/WNL.34.7.939View ArticlePubMedGoogle Scholar
 Mathis CA, Wang Y, Holt DP, Huang GF, Debnath ML: Synthesis and evaluation of ^{ 11 } Clabeled 6substituted 2aryl benzothiazoles as amyloid imaging agents. J Med Chem 2003, 46: 2740–2754. 10.1021/jm030026bView ArticlePubMedGoogle Scholar
 Logan J, Fowler JS, Volkow ND, Ding YS, Wang GJ, Alexoff D: A strategy for removing the bias in the graphical analysis method. J Cereb Blood Flow Metab 2001, 21: 307–320.View ArticlePubMedGoogle Scholar
 Blomquist G, Englar H, Nordberg A, Ringheim A, Wall A, Forsberg A, Estrada M, Franberg P, Antoni G, Langstrom B: Unidirectional influx and net accumulation of PIB. Open Neuroimag J 2008, 2: 114–125.View ArticlePubMedGoogle Scholar
Copyright
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.