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Clinical Investigations |
1 Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
2 Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
3 Department of Neurology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONREFERENCES |
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Key Words: PET • Alzheimer’s disease • amyloid • Pittsburgh Compound B
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONREFERENCES |
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This paper describes our efforts to extend the quantitative PIB studies of Price et al. (9) to include an evaluation of simplified methods of analysis with 9 additional subjects (n = 24). The simplifications included a shorter scan duration, the use of image-derived cerebellar or carotid time–activity data in lieu of an arterial input function, and a single late-scan SUV-based method.
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MATERIALS AND METHODS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONREFERENCES |
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Imaging
MRI and PET was performed as previously described (9). Briefly, PET data were acquired using a Siemens/CTI ECAT HR+ scanner (3-dimensional [3D] mode, 63 image planes, 15.2-cm axial field of view [FOV]) after the injection of 370–555 MBq of high-SA (>21.4 GBq/µmol) PIB. PET data were acquired over 90 min (34 frames: 4 x 15 s, 8 x 30 s, 9 x 60 s, 2 x 180 s, 8 x 300 s, 3 x 600 s). Emission data were corrected for attenuation, scatter (11), and radioactive decay and reconstructed using filtered backprojection. The reconstructed image resolution was 
6-mm full width at half maximum in the transverse and axial planes. Test–retest variability was assessed for 8 subjects (Table 1) who completed a second PIB imaging session within 8–28 d of the baseline study. Before the PET session, a spoiled gradient recalled (SPGR) MR scan was obtained for each subject for MRI/PET image coregistration and anatomic region-of-interest (ROI) definition. A 2-component MRI-based partial-volume correction was used to correct the PET data for the dilutional effect of expanded cerebrospinal fluid (CSF) spaces associated with normal aging and disease-related atrophy (12,13).
Image Coregistration and ROI Definition
MRI/PET image registration was performed using the automated methods of Minoshima et al. (14) and Woods et al. (15), as previously described (9). Volumes of interest (VOIs) were defined on the coregistered MR image and included sensory motor cortex (SMC), subcortical white matter (SWM), frontal cortex (FRC), anterior cingulate gyrus (ACG), posterior cingulate gyrus/precuneus (PCG), lateral-temporal cortex (LTC), mesial-temporal cortex (MTC), parietal cortex (PAR), pons (PON), caudate (CAU), and cerebellum (CER). The MTC region included the amygdala, hippocampus, entorhinal cortex, and other portions of the parahippocampal gyrus (Fig. 1). VOIs defined on coregistered MR images were used to sample the dynamic PET data to obtain regional time–activity radioactivity concentrations based on a calibrated phantom standard.
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Image-Based (Carotid) Arterial Input Function.
PET images were summed over the first 45 s after injection when radioactivity concentration in the vasculature is greatest and best visualized. ROIs were traced on the summed images around the left and right internal carotid arteries, across 3 contiguous planes. The resulting VOIs were used to sample the dynamic PET data and to generate carotid time–activity curves. For this study, metabolite correction of the carotid time–activity data was performed in a manner analogous to that described for the analysis of the dopamine D2 receptor radioligand 18F-fallypride (16) by applying a population average (n = 24) of the unchanged fraction of PIB that was determined from arterial sampling.
Data Analysis
Simplified methods of analysis were applied to the time–activity data and compared with Logan graphical analysis (17) with arterial input function data and 90 min of data acquisition (ART90). This method was selected as the benchmark as it was found to provide robust and reproducible measures of PIB retention in a prior comparative analysis of arterial-based methods (9). This previous study also indicated that the CER region was an appropriate reference region for the PIB data analyses, as no group differences in CER DV values were observed. Furthermore, the CER has been shown to contain negligible levels of fibrillar amyloid (18) and similarly low levels of PIB binding in postmortem binding studies of control, severe AD, and non-AD demented subjects (2,6). The regional results were normalized to the results obtained in the reference region to minimize the influence of nonspecific effects.
The simplified methods included analyses over a shorter PET scan duration and use of image-derived estimates of radioactivity concentration in CER tissue and carotid arteries as input to analyses that are appropriate for reversible ligand-binding kinetics (9).
Outcome Measures.
The data were analyzed by summing PET data, graphical analyses, or the simplified reference tissue method (SRTM) to respectively yield outcome measures of the SUV (unitless), DV (mL/mL), (DVR, unitless), and binding potential (BP, unitless). The DV and BP measures can be directly related to the free binding-site pool (Bmax) and the ligand dissociation constant (Kd) (19). The DVR is closely related to the BP and can be computed from DV estimates for a region (DVROI) and reference tissue (DVREF), where DVR = DVROI/DVREF = BP + 1. To facilitate the direct comparison of simplified methods the DVR was selected as a common outcome measure, as it provides a reliable nonnegative measure across AD, MCI, and control subjects. To express all results (with the exception of the SUV ratio [SUVR]) in terms of DVR required the derivation of this parameter by 1 of 3 methods: (a) as the ratio of regional and CER DV values (arterial and carotid input); (b) from the slope of the linear regression of graphical variables that included CER time–activity data as an implicit representation of the input function (Logan graphical analysis, CER input); and (c) by adding 1 to the direct estimation of the BP parameter (SRTM, CER input).
SUV.
The SUVR was determined using the summed regional radioactivity concentration (ROIsum) determined over 40–60 min (SUVR60) or 40–90 min (SUVR90) after injection, injected dose (ID), and participant mass normalized to the CER SUV value: SUV = ROIsum (kBq/g)/[(ID (kBq)/mass (g)] (20). This calculation assumed an average brain tissue density of 1 g/mL. The SUVR measures are equivalent to simple late-scan tissue ratios, as the common factors of ID and body mass cancel.
Logan Graphical Analyses.
To assess specific PIB retention, linear graphical regression analyses were applied. Linear regression of the Logan graphical variables yields slope values that are equivalent to the total radiotracer DV (17). A variation of the Logan graphical analysis allows for the substitution of image-derived reference tissue data in place of the plasma radioactivity input function, yielding slope values equivalent to the radiotracer DVR (21).
The graphical analyses were performed for both the 35- to 60-min (5 points) and 35- to 90-min (8 points) intervals using input functions derived from arterial sampling (ART60, ART90), carotid VOI placement (CAR60, CAR90), and CER tissue radioactivity (CER60, CER90). The performance of the graphical analyses was assessed on the basis of the value of the regression correlation coefficient (r2), which reflects the extent to which the data agreed with the basic method assumptions. The CER90 method was also implemented using a constraint for the tissue efflux constant, k2, that was fixed to an average CER value of k2 determined from 2-tissue compartment, 4-parameter (2T-4k) compartmental analyses (
2 = 0.149 min–1; data not shown) (9).
Parametric images were generated by applying the Logan analysis on a voxel-by-voxel basis (35- to 90-min interval) to PET images that were coregistered to the MR data. No CSF correction was applied to the parametric images.
SRTM.
SRTM is an image-based analysis method that uses reference ligand kinetics in a reformulation of the compartmental model equations that does not require an arterial input function (22). Application of SRTM to dynamic PET data results in the estimation of 3 parameters: the BP ([Bmax/Kd]f2), the local rate of radiotracer delivery relative to the reference tissue (RI), and the effective tissue efflux rate constant (k2). A basis function method was used to estimate these parameters from regional and CER time–activity data (23). Boundary values for the nonlinear term 
3 were set by determining the maximum BP value and the minimum and maximum observed rates of tissue clearance (k2) across all regions and subjects from prior compartmental analyses of a subset of the subject group (n = 15; data not shown) (9). Constraining 3 to the range 0.0007/s < 3 < 0.004/s was found to be a suitable choice for the analysis of PIB data using the basis-function implementation of SRTM.
Statistical Methods.
Descriptive statistics included the arithmetic mean, coefficient of variation (CV [%] = (SD/mean)·100), and the coefficient of determination (r2). Least-squares linear regression was used to determine the correlation between simplified outcome measures and those obtained using ART90. The strength of the correlation is expressed in terms of r2, and in each case the same number of points (n = 24) were included in the regression. The statistical significance of group differences was tested for each ROI using the Wilcoxon signed rank test (
= 0.05, 1-sided, exact inference), which was used (rather than ANOVA) because of the small sample sizes and violation of the assumption of normality (24). The P values for group differences were corrected for multiple comparisons using a false-discovery-rate (FDR) correction (25). The nonparametric Wilcoxon test minimizes the influence of outliers as it is based on the rank order of the subjects in the comparison groups rather than the magnitude of differences in group mean values. As a result, this method imposes a minimum possible P value for the dataset and yields conservative P values that might be considerably more significant using parametric methods, such as the Student t test. In the present study, the smallest attainable P value was P = 0.001.
The intrasubject variability measures used herein are described in detail by Price et al. (9). Briefly, the test–retest variation was assessed using a percentage difference measure corresponding to the absolute value of the percentage difference between retest (R) and test (T) DVR values: Test/retest (%) = absolute{[(R – T)/T]·100}.
Cohen’s effect size (d), which is a statistical index of the difference between 2 groups, was calculated as the difference between the group mean values normalized to the pooled SD of the 2 groups (26). An effect size of zero indicates complete overlap of the 2 groups, whereas increasingly larger effect sizes indicate differences that are of greater statistical significance. Although the assumption of normality may not be adequately met for these small sample sizes, the effect size measure allowed us to examine the potential capability of a given method to discern differences between AD and control subjects for future larger studies. The effect size computed herein was determined using the pooled SD and corrected for artifacts that can arise in small sample sizes (27). The pooled SD was deemed valid on the basis of similarity of the control and AD relative SDs (P > 0.2).
Evaluation Criteria
The simplified methods were evaluated with respect to the following criteria:
Rank Order.
Rank order of the DVR or SUVR values was examined with respect to region and subject. For each region and subject group, the average outcomes were ranked in descending order (1 = highest, 11 = lowest). The regional rank order was examined for its consistency with the expected deposition of amyloid in AD and for its consistency across methods. The subject rank order was also compared across methods to verify consistency in the ordering and classification of subjects across the methods.
Test–Retest Variability.
The intrasubject variability in the test–retest data was evaluated using the test–retest percentage difference.
Bias and Correlation.
The ART90 DVR was used as the standard benchmark measure of PIB retention for this work. A percentage bias measure was determined for all methods as the difference between the simplified DVR (or SUVR) and the ART90 DVR, normalized to the ART90 value: % bias = [(Method –ART90)/ART90] x 100%. To examine whether the percentage bias was uniform across the dynamic range of a method, the mean percentage bias ± SD was calculated for both low-DVR subjects (ART90 PCG DVR < 1.8; n = 13) and high-DVR subjects (ART90 PCG DVR > 1.8; n = 11). The low-binding subjects were all controls and M-2, -5, -6, -9, and -10. The high-binding subjects were all AD and M-1, -3, -4, -7, and -8. Correlations between the ART90 method and the simplified methods were calculated using linear regression analysis (r2 and slope).
Effect Size.
The impact of intersubject variability on the determination of significant group differences was examined for PCG, FRC, PON, and MTC regions, using the Cohen’s effect size metric (26).
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RESULTS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONREFERENCES |
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4 on average. At later times (>5 min), the carotid input function reflected the shape of the arterial input function more closely, converging somewhat with the latter to maintain a constant ratio between the methods of 2.
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Tissue Data.
Tissue-to-CER radioactivity concentration ratios were computed for each brain region. In PCG, which showed the highest degree of PIB retention in AD subjects, the VOI-to-CER ratios reached a plateau at a value of approximately 2.5:1 after 45 min. Control subjects maintained ratios of approximately 1:1 for all primary amyloid-binding areas (Fig. 3). On average, the tissue-to-CER ratios began to plateau at about 35 min in controls and 45–50 min in AD subjects.
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SUV.
The single (summed) late-scan tissue ratios that were computed over either 40–60 min (SUVR60) or 40–90 min (SUVR90) were found to be in agreement for both the AD and the control subject groups. In controls, regional SUVR60 ratios ranged from 1.11 ± 0.13 (CAU) to 1.80 ± 0.13 (PON), whereas SUVR90 tissue ratios ranged from 1.14 ± 0.13 (CAU) to 1.76 ± 0.14 (PON). In AD subjects, the regional SUVR60 values ranged from 1.38 ± 0.19 (MTC) to 2.80 ± 0.28 (PCG) and the SUVR90 values ranged from 1.40 ± 0.20 (MTC) to 2.88 ± 0.30 (PCG).
Logan Graphical Analyses.
The Logan graphical analysis generally provided estimates of DV (arterial or carotid input) and DVR (CER input) values with high regression correlations (r2) in 10 of 11 regions that generally exceeded 0.99. For the SWM, correlations were generally lower (0.7 < r2 < 0.99) than for other regions, particularly when the dataset was truncated to 60 min.
Parametric images of DVR measures obtained using the ART90 and CER90 analyses show similar patterns and levels of PIB retention (Fig. 5) in a healthy control (C-4), a control with evidence of FRC amyloid deposition (C-2), an MCI subject with no significant amyloid deposition (M-2), an MCI subject with intermediate levels of PIB retention (M-10), an MCI-subject with a characteristic AD pattern of PIB retention (M-4), and a representative AD subject (A-2).
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Evaluation Criteria
Rank Order.
The regional rank order of outcome measures averaged for the 6 AD subjects was well conserved across all 9 simplified methods, as each identified PCG as the region with the greatest PIB retention, followed by ACG and other cortical regions, including PAR, FRC, and LTC (Table 4). PIB binding in CAU exceeded that of SMC, OCC, and MTC. White matter–containing regions, such as PON and SWM, were among the lowest in terms of regional rank order in AD subjects. In control subjects, white matter–containing regions such as PON and SWM occupied the highest ranks.
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Among the control subjects, the ART90 DVR values indicate subjects C-1 and C-6 as having elevated levels of PIB retention relative to other controls in PCG, whereas subjects C-1 and C-2 appear to have elevated PIB retention in FRC (Figs. 4A and 4B). All simplified methods examined distinguished C-1 from other controls in both PCG and FRC and C-2 in FRC. However, only ART60 agreed with ART90 with regard to the elevated status of C-6. Interestingly, inspection of the late summed PET images showed only subjects C-1 and C-2, among controls, to have a visually discernible pattern of cortical PIB retention indicating FRC amyloid deposition (Fig. 5).
Test–Retest Variability.
Table 5 summarizes the variability measures and shows that favorable margins of test–retest variability were observed that were generally within ±10% across methods and regions, except for SWM (6.0%–23.8%). For most regions, the CER60 and CER90 methods showed the lowest test–retest variability with averages within ±4.4% and ±4.6%, respectively. Interestingly, the CER-based SRTM90 method showed somewhat greater variation than either CER60 or CER90, averaging ±6.2% across all regions. The SUV-based methods were reproducible as well, averaging ±5.3% and ±5.0% across regions for SUVR60 and SUVR90, respectively. The greatest test–retest variability was observed for the arterial- based methods. Greater variability was observed with a shorter scan duration, as is the case for CAR60 (±12.9%) and ART60 (±9.2%), whereas that for the 90-min measures was less (ART90, ±6.9%; CAR90, ±7.1%).
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All methods consistently separated control and AD groups and resulted in large Cohen’s effect sizes for regions with high PIB retention. The greatest Cohen’s effect sizes (d) were observed in the PCG and ranged from about 6.9 (SUV methods) to 4.6 (SRTM90). The magnitude of the effect sizes reflects that clear separation of mean PIB retention values is achieved between control and AD subjects. Table 6 lists the range of effect sizes in PCG, FRC, MTC, and PON. The PON region is not expected to differ between AD and control subjects and, thus, has an effect size that varies about zero.
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONREFERENCES |
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Shortened Scan Interval
In general, analysis methods that used 90 min of emission data performed better than when 60 min was used, although 60 min of emission data yielded useful data, as judged by the evaluation criteria. The most notable exception was the application of SRTM using 60 min of data, which resulted in spurious values, high intersubject variability, and aberrations of regional rank order. A shorter scan duration was associated with substantially higher test–retest variability in the case of ART60 and CAR60 (Table 5), although for ART60 this measure was still within the ±10% margin generally considered acceptable for most PET radiotracers (28,29). This may be of greatest concern for longitudinal studies that require reliable repeated measures of PIB retention. Truncation to 60 min did not result in a significant change in the level of methodologic bias for CAR60 or SUVR60, but CER60 showed a larger negative percentage bias compared with CER90 (Fig. 6A). Intersubject variability was only substantially higher in the ART60 and CAR60 datasets in SWM, which is likely a result of the failure to reach tissue-to-plasma equilibrium in this brain region. In general, effect size (with respect to the separation of AD and control subjects) was not adversely affected by truncation of the dataset to 60 min (Table 6).
Carotid VOI-Derived Arterial Input Function
Important limitations of the carotid VOI methods include the inability to assess the unchanged fraction of PIB in plasma and the inherent underestimation of the arterial input function as a result of partial-volume averaging effects. The latter resulted in a systematic overestimation of regional and CER DV values by approximately a factor of 2 (e.g., CAR90 CER DV in Table 3). Other studies have applied corrections to address the underestimation of peak plasma radioactivity in a carotid VOI that results from partial- volume averaging and coarse temporal sampling (30,31). No corrections were made to recover the peak radioactivity concentration in the present study. Nevertheless, the carotid VOI methods provided regional DVR estimates that most closely reflected ART90 DVR values and were the least biased relative to ART90 for both low- and high-DVR subjects (Figs. 6A and 7A). This suggests that the use of a population average metabolite correction may be an appropriate substitute for individual data and that CER normalization effectively reduces the impact of the carotid VOI bias on the regional DVR measures. Studies are underway to assess whether radiometabolite correction factors determined from venous blood can effectively replace arterial-based correction factors on an individual basis.
The CAR90 results were very comparable to ART90 in terms of test–retest variability (7%; Table 5) and effect size (5; Table 6). Despite the fact that the ART90 and CAR90 effect sizes were among the smallest of the 9 methods studied in this work (mostly due to higher SDs of the group means), these methods yielded very robust group differences that effectively distinguished AD and control subject groups. To put this in perspective, the use of a parametric test of significance (2-tailed t test) showed that even the lowest PCG effect size of 4.6 corresponded to a highly significant difference between the AD and control group means with a P value of <0.0000001. It is important to note that these 2 methods (ART90 and CAR90) may share inaccuracies generated by the use of arterial-based metabolite corrections, although the influence of individual inaccuracies should be minimized by the use of the population average metabolite correction in the CAR90 method. It is acknowledged that full realization of this simplification in future studies will require further validation of the population approach, as the present sample size lacks sufficient statistical power to evaluate potential group differences in the metabolism of PIB.
Reference Tissue-Based Input Function
The CER60 and CER90 methods resulted in DVR estimates that were negatively biased with respect to ART90 DVR measures (Fig. 6), particularly in high-DVR subjects. This bias appeared to be unrelated to the tissue efflux constant k2, as it remained whether or not a constraint was imposed based on the population average k2 value. As suggested by Logan et al. (21), the 2 constraint in the noninvasive Logan analysis may be omitted without resulting in a significant bias in the DVR measure when the ratio of the target to the reference tissue radioactivity concentration (C(t)/Cr(t)) remains constant for a protracted period. For PIB, this condition appears to be satisfied, as evidenced by stable tissue-to-CER ratios after 45 min in high-DVR regions (Fig. 3). The negative bias observed in high-DVR subjects using CER input methods likely reflects relative differences in CER and plasma clearance rates (32). This effect appears to be less important in subjects with lower levels of amyloid deposition. Previous fully quantitative PIB studies showed that the CER data were inadequately described by a 1-tissue (2 parameter) compartment model and required 2 tissue compartments. Although this fact raises concern regarding the application of SRTM for the analysis of PIB data, SRTM90 DVR values were slightly less biased in high-binding subjects compared with CER90 and considerably less biased relative to CER60.
The noninvasive Logan methods (CER60 and CER90) showed the lowest test–retest variability of any method examined (5%) across all regions. SRTM90 showed slightly higher test–retest variability (6% across regions), though this level of variability would be considered to represent a satisfactory level of performance for a PET agent. Intersubject variability in the control group was substantially higher for SRTM90 than for either CER60 or CER90, although in the AD group the methods were more comparable. This fact largely explains the larger effect sizes observed for CER60 and CER90 compared with that for SRTM90.
Late Single Scan Measure
Because of its simplicity, the SUV measure is frequently used in clinical studies where it can be impractical to use quantitative analysis methods that require dynamic imaging or input function determination. To eliminate a major source of variability in the determination of SUV, the time interval for the evaluation of the SUV parameter must be chosen such that the change in the SUV value over the interval is relatively small in comparison to the SUV value itself (33).
In the case of in vivo PET studies, the SUVR reflects the relative contributions of specific and nonspecific binding to the measured signal and, therefore, is more comparable to the DVR value. For the PIB data, the ratio of tissue (amyloid containing) to CER radioactivity was relatively constant beyond 40 min after injection in both AD and control subjects (Fig. 3) and, therefore, consistent with the determination of the SUV ratios after this time. The ratio also eliminates other sources of variability, such as body composition and inaccuracies in determining the ID (e.g., partial extravasation), which may adversely impact the calculation of SUV (20). Both SUVR60 and SUVR90 showed strong positive biases in PCG relative to ART90 that were similar in the low- and high-DVR subjects (Fig. 6A). Test–retest variability for the SUVR methods was among the lowest of the methods examined, likely a result of the cancellation of mutual sources of variability in the ratio (32). The SUV- based methods showed the greatest dynamic range and mean difference between control and AD subjects of all methods examined, which, coupled with reasonably low intersubject variability, produced the largest effect sizes of any method as well (Table 6).
Reference Region Considerations
It must be acknowledged that all of the reported methods share a common potential shortcoming in their reliance on CER measures of PIB retention, whether it be in the implicit representation of the input function (CER90, CER60, SRTM90) or in the computation of the DVR outcome measure. As advanced AD subjects were not included in this investigation, the degree to which CER fibrillar amyloid deposits in these advanced subjects will contribute to the DVR or SUVR outcome measure is not known. Of the methods that do not require arterial sampling, the carotid-based methods are advantageous in that it is possible to assess CER PIB retention on an individual basis. In the event that CER amyloid emerges as a concern in more advanced AD subjects, it may be necessary to consider alternative methods of assessing PIB retention across the AD disease spectrum. Such alternatives may include statistical methods that account for differences in nonspecific binding through covariate adjustment and consideration of other potential sources for the reference kinetics. In addition, the assessment of CER PIB retention requires careful consideration of subject placement in the PET scanner given the nonuniform axial sensitivity of PET tomographs operating in 3D mode. A further consideration is the placement of the CER VOI, as any artifacts generated by erroneous CER VOI placement (e.g., contamination of PIB CER measure by adjacent white matter signal) could contribute bias to regional DVR and SUVR outcome measures.
Evaluation of Putative Emerging Cases
Several individual cases afford a special opportunity to cross-check the consistency of methods with respect to subjects whose PIB retention measures suggest subtle amyloid deposition. Figure 4 indicates that patients C-1, C-2, M-6, and M-10 invariably fall at or above the upper limits of the control range in the PCG or the FRC. C-6 often falls near this upper end as well and will be discussed separately below. Visual inspection of the parametric and late-summed images in these cases reveals mild, often focal, but convincing patterns of cortical PIB retention that are consistent with those seen in AD subjects. C-2 and M-10 show predominantly frontal retention, although M-10 has more widespread retention. The retention in C-2 is mostly seen in the left FRC. C-1 and M-6 show a mild, generalized pattern that is most apparent in the PCG, with the retention in M-6 being more apparent than that in C-1. No method stands out as being obviously superior in segregating these cases from the remaining controls, although the SUVR methods may perform somewhat better in this regard. C-6 is unusual because the elevation above the other controls is observed only with the arterial-based methods (ART60/90 more so than CAR60/90) and not with the CER- or SUV-based methods, suggesting that this difference was related to the arterial input function of C-6. This case demonstrates that discrepancies in subject rank between arterial and CER methods can arise that might be explained by either an erroneous estimate of the arterial input function or true physiologic variability to which the CER-based methods are less sensitive. Review of the C-6 input function showed unusually rapid plasma clearance of PIB that did not appear to be attributable to a technical error.
Selecting a Method of Choice
Each method has certain advantages and disadvantages for specific purposes. Selection of a method of choice will depend on the nature of the particular application. All methods proved very capable of distinguishing AD and control subjects, but the SUVR90 method showed a small advantage in effect size. When a short scan time is most desirable (e.g., severe AD patients), the SUVR methods are advantageous because PIB retention measures can be obtained with the subject in the scanner for a period as short as 20 min (SUVR60). Other advantages of the SUVR method include simplicity of application (making it more applicable to routine clinical studies), superior PCG effect size (6.9), very good test–retest reproducibility (5.0%), and a large dynamic range. In studies that correlate amyloid load with other variables (e.g., neuropsychologic measures), the method with the most uniform bias across the full range of amyloid deposition (i.e., CAR90) is likely to perform best—although at the expense of higher test–retest variability. Test–retest reliability is most important when longitudinal examinations of PIB retention are made in the same subject to study the natural history of disease progression or the response to antiamyloid therapies. Although the CER methods (CER60/90) have the best test–retest reliability, one must again consider whether this advantage is offset by the inherent bias in these methods. If the bias that accompanies the use of CER as input is unacceptable, it may be necessary to use the CAR90 method. However, the superior test–retest variability of CER90 makes it an attractive method for detecting small treatment effects, particularly in cases with low levels of amyloid deposition that must ultimately be the principal target of these therapies.
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CONCLUSION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONREFERENCES |
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ACKNOWLEDGMENTS |
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FOOTNOTES |
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For correspondence or reprints contact: Brian J. Lopresti, BS, Department of Radiology, PET Facility, 200 Lothrop St., Room B938, Pittsburgh, PA 15213.
E-mail: loprestibj{at}upmc.edu
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, Individual subject values. (B) Slopes of linear correlations between ART90 and simplified methods. (C) Coefficient of determination (r2) for correlations between ART90 and simplified methods.
, solid line) and ART90 and CER60 (
, solid line) outcome measures. The equation describing the linear regression is shown for each comparison in the form y = mx + b as well as the coefficient of determination (r2). The thin dashed line in both graphs represents the line of unity.