Tau positron emission tomography (PET) is increasingly used in the clinical evaluation of patients and as an outcome measure in Alzheimer’s disease (AD) clinical trials. Due to differences in tracer properties, instrumentation, and methods of analysis, however, tau-PET outcome data cannot currently be meaningfully compared or combined. Here, we tested i) the feasibility of adapting the Centiloid method—an approach originally developed to standardize amyloid PET—to harmonize tau-PET quantification (CenTauRs); ii) the performance of a non-linear mixed model-based approach (Joint Propagation Model) that does not require the use of a reference tracer. Head-to-head tau-PET data (Table 1) was included from two cohorts ([ 18 F]RO948 vs [ 18 F]flortaucipir, n = 37 [BioFINDER-2]; [ 18 F]flortaucipir [Avid A05] vs [ 18 F]MK-6240, n = 15, University of Pittsburgh) in which each participant was scanned with two tau-PET tracers. Standardized uptake value ratio (SUVR) values were calculated using the inferior cerebellar cortex as the reference region. Anchor point data (Table 1) was derived for each tracer using the following criteria: CenTauR-0, cognitively unimpaired (CU), amyloid PET negative (<10 Centiloids); CenTauR-100, amyloid PET positive (>50 Centiloids), typical (temporoparietal) AD pattern on tau-PET visual read, age<65 andMMSE>20. Regions-of-interest (ROIs) included a universal tau-PET ROI—based on the intersection of tracer specific ([ 18 F]flortaucipir, [ 18 F]MK-6240, [ 18 F]PI-2620, [ 18 F]PM-PBB3, [ 18 F]GTP1 and [ 18 F]RO948) masks that had been derived by subtracting average of amyloid-negative CU images from the average AD image—as well as four subregions delineated within this ROI (medial temporal, meta-temporal, temporoparietal and frontal) (Figure 1A). An overview of the adapted Centiloid-like approach and the joint propagation model is shown in Figure 1B. High R 2 values were observed between tracers across all ROIs: [ 18 F]RO948 vs [ 18 F]flortaucipir, average 0.965 [range 0.923 (frontal) to 0.986 (universal)]; [ 18 F]MK-6240 vs [ 18 F]flortaucipir, average 0.985 [range, 0.923 (medial temporal) to 0.991 (frontal)]. The Centiloid-like and joint propagation model approaches provided near identical CenTauR values (Figure 2). Preliminary findings support the development of standardized scale for tau-PET using both a Centiloid-like or joint propagation model approach. Additional data and consideration of the advantages and disadvantages of each will be needed to recommend one approach over the other.

Compared with biofluid-biomarkers, tau-PET shows stronger association with cognitive impairment (Ossenkoppele,R-2021) and decline, and better detects Alzheimer’s disease (AD) pathology (Coomans,EM-2022; Smith,R-2022). Conventionally, tau-cognition relationship has been evaluated between global cortical tau-PET and composite cognitive scores. However, tau-PET binding patterns are heterogenous, with regional binding showing strong correlations with domain-specific cognitive performance and decline. The goal of this study was to evaluate domain-specific patterns of tau-PET at baseline and in change-from-baseline (CFB) in symptomatic AD patients. Amyloid-positive participants (n = 172) with a clinical diagnosis of mild-cognitive-impairment (n = 97) or dementia due to AD (n = 76) and amyloid-negative healthy-controls (n = 68) underwent a 18 Flortaucipir tau-PET and neuropsychological testing (AV1451-A05:NCT02016560) at baseline and 18months. Observed cognitive scores in impaired patients were normalized to age-adjusted outcomes in healthy-controls at baseline and 18months (Ossenkoppele,R-2015). These scores were subsequently averaged within the episodic-memory, semantic-memory, language, visuospatial, and executive function cognitive domains (Malpetti,M-2022). Standardized uptake value ratio (SUVr) in automated-anatomical-labelling regions (Tzourio-Mazoyer,M-2002) and AD-specific region (MUBADA; Devous,M-2017) was calculated in reference to cerebellum-crus. The CFB (18months-baseline) in SUVr and domain-specific sub-scores were calculated for all participants. Correlations between global SUVr versus composite cognitive score and regional SUVr versus domain-specific scores at baseline and CFB were calculated. Differential cross-sectional tau-PET patterns showed significant negative associations with domain-specific cognitive performance (Fig.1A). When evaluated longitudinally, higher global CFB in tau-PET SUVr showed no-to-modest correlation with cognitive decline (Fig.2: ADAS-Cog11 = 0.012, FAQ = -0.015, MMSE = -0.210, CDR-SB = 0.014), however, heterogeneous associations were observed at the regional- and voxel-level (Fig.1B,Fig.3), ranging from maximum of r = -0.45 (p<0.001; frontal-inferior and visuospatial) to minimum of r = -0.002 (p<0.1; lingual and episodic). Increase in tau-PET signal was associated with greater cognitive decline (i.e. visuospatial function: predominantly frontal; executive function: parietal and prefrontal; semantic-memory: superior temporal region). An extensive and asymmetric tau-PET pattern (L>R) in frontal and occipital regions was related to language. We found associations between patterns of tau accumulation and domain-specific cognitive decline in MCI and AD dementia. After validations using larger datasets, these relationships can supplement widely used associations between global tau-PET and composite cognitive scores and, therefore, enhance tau PET-guided and patient-centered staging, prognosis, and response assessment.

Thursday, April 7May 3, 2022Free AccessTRAILBLAZER-ALZ 2: A Phase 3 Study to Assess Safety and Efficacy of Donanemab in Early Symptomatic Alzheimer’s Disease (P18-3.005)Jennifer Zimmer, Paul Solomon, Cynthia D. Evans, Ming Lu, John R. Sims, Dawn A. Brooks, and Mark A. MintunAuthors Info & AffiliationsMay 3, 2022 issue98 (18_supplement)https://doi.org/10.1212/WNL.98.18_supplement.1688 Letters to the Editor

Zagotenemab (LY3303560), a humanized monoclonal antibody targeting extracellular aggregated tau, is currently in development as a potential disease modifying treatment for early symptomatic Alzheimer’s disease (AD). The PERISCOPE‐ALZ study (Phase 2, NCT03518073) of zagotenemab implements tau PET for classifying AD pathological stage based on the NIA‐AA guidelines (Jack CR, Jr., Bennett DA, Blennow K, Carrillo MC, Dunn B, Haeberlein SB, et al. NIA‐AA Research Framework: Toward a biological definition of Alzheimer's disease. Alzheimers Dement. 2018;14(4):535‐62) and serves as a key eligibility criterion. Here we summarize the study’s screening and baseline characteristics.

LY3002813 (N3pG) is a humanized IgG1 antibody directed at a truncated N-terminally pyro-glutamate modified amyloid-beta protein (Aβ) epitope specifically localized in amyloid plaques. N3pG was developed to remove existing amyloid plaques through microglial mediated clearance. LY3202626 (BACEi) is a potent oral inhibitor of beta-site amyloid precursor protein-cleaving enzyme 1. N3pG and BACEi combination treatment in APP transgenic mice led to synergistic clearance of amyloid deposits relative to the monotherapies alone. Dual targeting of soluble Aβ (BACE inhibitor) and plaque Aβ (N3pG antibody) is hypothesized to substantially reduce all potentially pathological Aβ species from the brain, achieving maximal target engagement of the amyloid pathway. The ongoing Trailblazer-ALZ study is the first combination therapy trial of potential disease modifying therapies in AD. This study implements the new NIA-AA guidelines for AD classification based on biomarker-defined pathological stage of disease incorporating amyloid PET and tau PET, with a focus on identifying an early symptomatic AD population, achieving high levels of target engagement of the amyloid pathway, and incorporating sensitive clinical outcome measures. Trailblazer-ALZ (NCT03367403) is enrolling 375 participants with early AD by clinical and biomarker criteria into three study arms (in a 1:1:1 ratio) testing double-dummy placebo against monthly intravenous dosing of LY3002813 (N3pG) by itself and in combination with oral daily dosing of LY3202626 (BACEi). Patients are followed for 18 months, with florbetapir F18 PET scans every 6 months. The primary outcome measure is a composite scale of cognition and function – the Integrated Alzheimer's Disease Rating Scale (iADRS). Individualized dosing of LY3002813 is being applied to ensure optimal plaque removal for each patient. The study has >80% power to detect treatment effect assuming active arm having true effect of 50% slowing of iADRS progression compared to placebo. Efficient combination therapy studies can be implemented to maximize target engagement of the amyloid pathway and assess proof of concept in biomarker-defined early AD patients.

Exploratory analyses of flortaucipir F 18 PET in the EXPEDITION3 phase 3 trial of patients with mild Alzheimer's disease (AD) revealed a potential utility of flortaucipir to stratify risk of cognitive decline (Mintun MA et al, AAIC-2017). Moreover, modeled regional trajectories demonstrated a stereotypical lobar sequence of tau accumulation (Shcherbinin S et al, AAIC-2017). The aim of this study was to develop a simple, quantitative tau PET-based patient classification algorithm based on sequential lobar accumulation of flortaucipir signal. The EXPEDITION3 trial enrolled amyloid-positive patients with mild AD (MMSE 20-26). Flortaucipir images were acquired for a subset of participants at baseline, week 40 and week 80. We quantified tau burden in baseline scans from the placebo arm (N=97), using average SUVR values in atlas-based lateral temporal, parietal and frontal lobes with respect to a white matter-based reference region (PERSI). Elevated tau burden in the temporal (T+), parietal (P+) and frontal (F+) lobes bilaterally was determined using a single positivity threshold. All possible lobar profiles were grouped into four stages 0-III (T-P-F-, T+P-F-, T+/-P+F- and T+/-P+/-F+), reflecting the stereotypical patterns of tau spread inferred from neuropathological studies. A mixed effects repeated measures method was used to characterize changes in global weighted SUVR (MUBADA), cognition and structural MRI. Lobar stages were strongly concordant with a global tau burden measured by MUBADA SUVR. On average, individuals belonging to more advanced lobar stages at baseline demonstrated numerically more rapid cognitive (MMSE, ADAS-Cog14 and iADRS) and neurodegenerative (whole brain and whole temporal lobe volumes) decline over 80 weeks follow-up. In these analyses, approximately 25-30% of the cases were classified to each of stages I-III, with a slightly lower proportion assigned to stage 0. If scans were staged using a more rigorous sequential classification (T-P-F-, T+P-F-, T+P+F- and T+P+F+), <5% scans remained unclassified.

Traditional neuroimaging analysis, such as clustering the data collected for the Alzheimer's disease (AD), usually relies on the data from one single imaging modality. However, recent technology and equipment advancements provide with us opportunities to better analyze diseases, where we could collect and employ the data from different image and genetic modalities that may potentially enhance the predictive performance. To perform better clustering in AD analysis, in this paper we conduct a new study to make use of the data from different modalities/views. To achieve this goal, we propose a simple yet efficient method based on Non-negative Matrix Factorization (NMF) which can not only achieve better prediction performance but also deal with some data missing in some views. Experimental results on the ADNI dataset demonstrate the effectiveness of our proposed method.

This presentation updates the binding profile of Flortaucipir (18F) (AKA [18F]AV-1451, [18F]T807) to AD brain derived PHF tau and 72 normal CNS proteins. On and off-target binding to AD and normal brain tissue is also presented. Flortaucipir was evaluated in functional competitive binding assays against a panel of 72 normal CNS proteins. Binding studies were carried out with AD brain derived PHF tau as well as AD and normal brain homogenates. Autoradiography (ARG) was performed with 20 μCi Flortaucipir (18F) in frozen brain sections, which were then exposed with FujiFilm Imaging Plates overnight and scanned with FujiFilm Imaging System FLA-7000. Micro PET/CT scans of Flortaucipir (18F) were obtained in normal rats to determine if the presence of MAO-A/B inhibitor Pargyline altered tracer retention. Saturation binding Kd is 0.56 ± 0.06 nM in AD brain derived PHF tau. In functional competitive inhibition assays, 67 of 72 tested CNS proteins has <50% inhibition at 10 uM flortaucipir, a concentration at least 5 logs above expected tracer levels in human brain. The IC50 is between 1 and 10 uM for the norepinephrine transporter, polyamine site, acetylcholinesterase, μ-opiate receptor, MAO-B and 0.57 μM for MAO-A. Saturable binding studies in normal brain homogenates from multiple donors and brain regions provided Kd values ranging from 8 to 18 nM. Furthermore, 3 different MAO-A inhibitors displaced Flortaucipir (18F) binding in competitive inhibition assays on the same tissue. In kinetic binding studies, koff for recombinant MAO-A is about 9 times greater than for PHF tau while kon rates are comparable. MAO-A/B inhibitor Pargyline had no effect on Flortaucipir (18F) micro PET-CT imaging of rat brains. Autoradiography confirmed retention of Flortaucipir in neuromelanocytes in substantia nigrabut not in striatum of any age. Flortaucipir binds potently to AD brain derived PHF tau. Taken together, our results indicate that binding of Flortaucipir (18F) to MAO-A is weaker than to PHF tau and are consistent with the absence of clinically important MAO-A binding patterns in human PET imaging. Striatum uptake of Flortaucipir (18F) seen in human PET/CT scans was not seen by ARG on post-mortem brain sections.

This study examined the feasibility of using quantitation to augment interpretation of florbetapir PET amyloid imaging.A total of 80 physician readers were trained on quantitation of florbetapir PET images and the principles for using quantitation to augment a visual read. On day 1, the readers completed a visual read of 96 scans (46 autopsy-verified and 50 from patients seeking a diagnosis). On day 2, 69 of the readers reinterpreted the 96 scans augmenting their interpretation with quantitation (VisQ method) using one of three commercial software packages. A subset of 11 readers reinterpreted all scans on day 2 based on a visual read only (VisVis control). For the autopsy-verified scans, the neuropathologist's modified CERAD plaque score was used as the truth standard for interpretation accuracy. Because an autopsy truth standard was not available for scans from patients seeking a diagnosis, the majority VisQ interpretation of the three readers with the best accuracy in interpreting autopsy-verified scans was used as the reference standard.Day 1 visual read accuracy was high for both the autopsy-verified scans (90%) and the scans from patients seeking a diagnosis (87.3%). Accuracy improved from the visual read to the VisQ read (from 90.1% to 93.1%, p < 0.0001). Importantly, access to quantitative information did not decrease interpretation accuracy of the above-average readers (> 90% on day 1). Accuracy in interpreting the autopsy-verified scans also increased from the first to the second visual read (VisVis group). However, agreement with the reference standard (best readers) for scans from patients seeking a diagnosis did not improve with a second visual read, and in this cohort the VisQ group was significantly improved relative to the VisVis group (change 5.4% vs. -1.1%, p < 0.0001).These results indicate that augmentation of visual interpretation of florbetapir PET amyloid images with quantitative information obtained using commercially available software packages did not reduce the accuracy of readers who were already performing with above average accuracy on the visual read and may improve the accuracy and confidence of some readers in clinically relevant cases.

To date, longitudinal [18F]-Flortaucipir PET images have been obtained for different diagnostic groups but only over a relatively short period of time. Our aim was to combine these data in a principled fashion to develop average trajectories of tau tracer accumulation across the Alzheimer's Disease continuum. Baseline, 9- and 18-month follow-up flortaucipir images were acquired from MCI (n=61, age=71.1±9.1, baseline MMSE=27.8±1.9) and AD (n=27, age=76.4±7.9, baseline MMSE=22.4±4.0) patients as well as from cognitively normal (CN) participants (n=51, age=68.4±10.4, baseline MMSE=29.5±0.5). SUVR values in 4 bilateral atlas-based cortical sub-regions as well as global weighted SUVR (MUBADA) were calculated with respect to a white matter-based reference region (PERSI). Our methodology followed previously developed models (e.g. Villemagne VL et al, Lancet Neurology, 2013) of amyloid accumulation. For each participant and brain region, annual rate of change was determined as a slope of the linear regression line plotted through three longitudinal measurements. Locally weighted scatterplot smoothing (LOWESS) was utilized to approximate a relationship between annual rate and baseline SUVR across all subjects and then integrated over time resulting in an average region-specific trajectory. Within the range of available MUBADA SUVR values (0.92–2.05), the LOWESS-processed (medium smoothing) annual rate monotonically increased as baseline SUVR went up. Integration of this graph resulted in an accelerating accumulation of MUBADA-measured tau signal over time. This trajectory suggested an average interval of approximately 9 years between SUVR=1.01 (mean value for CN Aβ- participants) and SUVR=1.17 (mean value for MCI Aβ+ participants) and approximately 5 years between SUVR=1.17 and SUVR=1.35 (mean value for AD Aβ+ participants). Regional analysis demonstrated spatially non-uniform tau accumulation, with a flat trajectory for the medial temporal region and sequentially increasing trajectories for lateral temporal, parietal and then frontal regions.

LY3002813 (LY), a humanized IgG1 monoclonal antibody, was engineered to reduce existing amyloid plaque in AD by targeting Aβp3-X—a truncated N-terminally pyro-glutamate modified amyloid beta peptide specifically localized to deposited plaque. In the PDAPP mouse model of cerebral amyloidosis, the murine version of the antibody removed amyloid plaque without increasing microhemorrhage risk (Neuron 2012;76:908-20). We conducted a Phase 1 study in AD patients to assess the safety, PK, and pharmacodynamics (amyloid plaque burden assessed by florbetapir PET) of multiple dose LY (ClinicalTrials.gov Identifier: NCT01837641). Amyloid-positive prodromal to moderate AD patients were administered a single IV dose of LY (0.1 mg/kg IV to 10 mg/kg IV) or placebo during the single ascending dose (SAD) phase, followed by a 12 week follow-up period. The same patients proceeded into the multiple-ascending dose (MAD) phase and were administered up to 4 additional monthly IV doses of LY (0.3 mg/kg IV to 10 mg/kg IV) or placebo, depending on the initial SAD cohort. Adverse events, vital signs, ECGs, clinical laboratories, and neurological exams were assessed throughout the study and for up to 84 days after last dose to characterize safety and tolerability. 37 patients received LY and 12 received placebo in the SAD/MAD cohorts. The 49 subjects had a mean age of 74 yrs (±8yrs) with 57% female and 43% male. 53% had a Clinical Dementia Rating (CDR)=0.5; 43% had CDR=1, and 4% had CDR=2. Subjects had florbetapir PET scans at screening and 7 months after the first dose (233±44 days from screening) for quantitation of change in cerebral amyloid burden. MRI assessments for amyloid related imaging abnormalities were performed at screening, four weeks after the single dose, and at the end of the multiple dose phase. PK and immunogenicity assessments were performed throughout the trial. Safety, pharmacokinetics (PK), and florbetapir PET after multiple dose administration of LY will be presented.

Recent findings suggest the potential utility of the PET radiotracer 18F-AV-1451 (18F-T807) to detect longitudinal change in tau burden. The goal of this study is to identify subgroup(s) of subjects who may show the most rapid longitudinal change in 18F-AV-1451 signal, and examine baseline clinical phenotypes and image patterns associated with these subgroups. We examined 18F-AV-1451 PET images acquired from 60 amyloid-positive MCI and AD subjects who underwent both baseline and approximately 9 month follow-up scans. As “progressors”, we considered a group with the highest 25% change (follow-up minus baseline) in global SUVR from a large ROI weighted predominantly by cortical regions and using cerebellum crus as a reference (dSUVR). For each group, the baseline clinical characteristics and imaging metrics (estimated Braak stage (Schwarz AJ et al, Brain, 2016, in press), estimated hippocampus:cortex ratio (Murray ME et al, Lancet Neurology, 2011), global and regional SUVR) were estimated. Clinical phenotypes for progressors (8 AD, 7 MCI, mean dSUVR=0.16) differed from those for non-progressors (16AD, 29 MCI, mean dSUVR=0.00). Progressors were on average younger and had higher ADAS-Cog at baseline (Figure 1). Progressors were also separated by baseline 18F-AV-1451 imaging metrics, having higher global SUVR, more advanced in vivo estimated Braak stage (4-6 vs 3-4) and lower hippocampus:cortex SUVR ratio (Figure 1). Baseline SUVR for individual regions (temporal, occipital, parietal, frontal) also separated two groups. Specifically, minimal frontal SUVR was observed for non-progressors (95% CI=1.02-1.12) while progressors demonstrated an elevated SUVR in frontal lobe (95% CI=1.21-1.52). The dichotomization based on the highest 50% increase in global dSUVR resulted in directionally similar but smaller differences between progressors (13 AD, 17 MCI, mean dSUVR=0.09) and non-progressors (11AD, 19 MCI, mean dSUVR=-0.01).

The anatomical binding profile of the PET radiotracer 18F-AV-1451 (18F-T807) in mild cognitive impairment (MCI) and Alzheimer's Disease (AD) is largely consistent with expectations from neuropathology, and kinetic analysis indicates behavior in high-uptake cortical regions consistent with specific binding. However, differential kinetic behavior of 18F-AV-1451 in various brain areas remains to be fully explored. We examined 0-100 minute 18F-AV-1451 images spatially normalized to MNI stereotactic space. We included 4 young cognitively normal (YCN) subjects, 5 old cognitively normal (OCN) subjects, 5 subjects diagnosed with MCI and 5 with AD. Kinetic modeling (PMOD v3.5) was performed using three different standard methods: Logan graphical analysis (LGAR), multi-linear reference tissue model (MRTM2) and simplified reference tissue model (SRTM2) methods with the cerebellum crustaneous as an input reference region. Binding potentials (BPnd), clearance rate k2 and the tracer delivery rate R1 for AAL atlas-based regions were further evaluated. We also generated voxel-wise BPnd and SUVR (80-100 minute) maps. For 34 AAL cortical sub-regions, the binding potentials BPnd calculated across subjects by all three methods correlated strongly with corresponding SUVR-1 values: r(LGAR)>0.93, r(MRTM2)>0.89 and r(SRTM2)>0.73. Moreover, the regions and pixels with SUVR≈1 corresponded to ones with negligible binding (BPnd≈0). The separation of the cortical time-SUVR curves from the corresponding cerebellum lines was different across diagnostic groups. AD subjects’ kinetics diverged from normal subjects at approximately 50-60 minutes, while the 80-100 minute window provided greater separation for MCI subjects. The relationships between estimated kinetic parameters differed between the posterior regions and striatal sub-regions as exemplified by the MRTM2 results in Table 1. A delayed 80-100 minute scan provided a reasonable substitute for a dynamic 0-100 minute acquisition for cortical regions. Substantial prolongations of this window (e.g., to better categorize cohorts or reach SUVR steady state) should be made with caution as noise for some scans started increasing at 50 minutes. The regional clearance rate k2 might be explored as an additional metric and could be esimtated from dynamic 80-100 (or 75-105) minute scans. Further dynamic studies are planned and additional evaluation of kinetic modeling methodologies is warranted.

The anatomical binding profile of the PET radiotracer 18F-AV-1451 (18F-T807) in mild cognitive impairment (MCI) and Alzheimer's Disease (AD) is largely consistent with expectations from neuropathology, and kinetic analysis indicates behavior in high-uptake cortical regions consistent with specific binding. However, differential kinetic behavior of 18F-AV-1451 in various brain areas remains to be fully explored. We examined 0-100 minute 18F-AV-1451 images spatially normalized to MNI stereotactic space. We included 4 young cognitively normal (YCN) subjects, 5 old cognitively normal (OCN) subjects, 5 subjects diagnosed with MCI and 5 with AD. Kinetic modeling (PMOD v3.5) was performed using three different standard, methods: Logan graphical analysis (LGAR), multi-linear reference tissue model (MRTM2) and simplified reference tissue model (SRTM2) methods with the cerebellum crustaneous as an input reference region. Binding potentials (BPnd), clearance rate k2 and the tracer delivery rate R1for AAL atlas-based regions were further evaluated. We also generated voxel-wise BPnd and SUVR (80-100 minute) maps. For 34 AAL cortical sub-regions, the binding potentials BPnd calculated across subjects by all three methods correlated strongly with corresponding SUVR-1 values: r(LGAR)>0.93, r(MRTM2)>0.89 and r(SRTM2)>0.73. Moreover, the regions and pixels with SUVR≈1 corresponded to ones with negligible binding (BPnd≈0). The separation of the cortical time-SUVR curves from the corresponding cerebellum lines was different across diagnostic groups. AD subjects’ kinetics diverged from normal subjects at approximately 50-60 minutes, while the 80-100 minute window provided greater separation for MCI subjects. The relationships between estimated kinetic parameters differed between the posterior regions and striatal sub-regions as exemplified by the MRTM2 results in Table 1. A delayed 80-100 minute scan provided a reasonable substitute for a dynamic 0-100 minute acquisition for cortical regions. Substantial prolongations of this window (e.g., to better categorize cohorts or reach SUVR steady state) should be made with caution as noise for some scans started increasing at 50 minutes. The regional clearance rate k2 might be explored as an additional metric and could be estimtated from dynamic 80-100 (or 75-105) minute scans. Further dynamic studies are planned and additional evaluation of kinetic modeling methodologies is warranted.