Synthetic FDG‐PET hypometabolism sensitivity validation in AD

BackgroundThe availability of 18‐F fluorodeoxyglucose positron emission tomography (FDG‐PET) is not universal. We hypothesized that synthetically generated FDG‐PET images would be as sensitive to detecting the pattern of hypometabolism associated with AD as real images.MethodWe developed a deep learning‐based method to produce synthetic FDG‐PET images from 1,828 T1‐weighted MRI / real FDG‐PET image pairs from the ADNI dataset, and validated the technique on a further 284 image pairs. The technique generated synthetic FDG‐PET images which were then processed to compare Standardized Uptake Value Ratio (SUVR) with the pons as reference in 81 brain regions as defined in the Desikan‐Killiany‐Tourville and subcortical default FreeSurfer atlases.ResultWe tested the differences between synthetic and real FDG‐PET on 745 image pairs (205 controls, 365 mild cognitive impairment (MCI) and 175 AD)(Table 1). Correlations in SUVR values between synthetic and real FDG‐PET ranged between weak (r = 0.13) to strong (r = 0.63), with moderate results in key regions for AD (bilateral precuneus, r = 0.43; bilateral posterior cingulate, r = 0.37). There were significant between‐group (control vs MCI and control vs AD) differences in SUVR values for all regions between synthetic and real PET‐FDG (Figure 1) with synthetic FDG‐PET having lower values. Inter‐group effect sizes were not significantly different in the majority of brain regions (76/81)(Figure 2), with similar effect sizes in the right precuneus (synthetic: ‐0.98 vs original: ‐1.37), left (‐0.77 vs ‐1.0459) and right (‐0.80 vs ‐1.04) posterior cingulate, but different for the left precuneus (‐0.91 vs ‐1.33).ConclusionSynthetic images would increase patients’ accessibility to a meaningful modality for disease assessment while decreasing their exposure to radiation and resources in the health care system.

BackgroundThe cognitive reserve (CR) hypothesis was proposed to explain inter‐individual differences in the association between observed cognitive function and estimated neurodegenerative burden, e.g. in Alzheimer’s disease (AD). Latent CR marker approaches were suggested as a promising method to predict reserve.MethodAmyloid‐β (Aβ)‐negative, cognitively normal and Aβ‐positive, cognitive impaired (Clinical Dementia Rating global score >= 0.5) individuals participating in the AD Neuroimaging Initiative (ADNI) were classified as healthy controls (HC, N=138) and AD spectrum (ADS, Baseline N=462, Follow‐Up N=211), respectively. A latent CR marker (CRM) was obtained by multilinear regression analysis in the entire study cohort, defined as the residual global cognitive performance remaining after accounting for neuropathological burden (CSF Aß42 and total Tau levels and APOE ε4 carrier status) and demographic variables. Mean standardized uptake value ratio (SUVR) values for regions of interests (ROIs) of left and right angular gyrus (L/R‐ANG), bilateral posterior cingulate (PCC), left and right inferior temporal gyrus (L/R‐TEMP) were derived from FDG‐PET data. In separate analyses for HC and ADS, the interactive effects of the memory cognitive composite (MEM) and latent CR marker on FDG‐PET SUVR values were tested for the five cortical ROIs at baseline and 24‐months follow‐up data.ResultIn ADS, higher CRM attenuated the relationship between MEM and FDG‐PET SUVR in L‐TEMP, R‐TEMP and L‐ANG at baseline, and in L‐TEMP, R‐TEMP, L‐ANG and R‐ANG in the longitudinal analyses. In HC, however, no interaction was observed.ConclusionPatients with higher CR maintained their memory performance better in the face of AD‐related glucose hypometabolism . Therefore, the present study supports the use of a residual CRM to improve our understand of inter‐individual differences in the resilience against neurodegeneration, both cross‐sectionally and longitudinally.

Background: Changes in the metabolic and structural brain networks in mild cognitive impairment (MCI) have been widely researched. However, few studies have compared the differences in the topological properties of the two brain networks assessed using magnetic resonance imaging (MRI) and fluoro-deoxyglucose positron emission tomography (FDG-PET) in patients with MCI. Methods: This study included 137 patients with MCI and 80 healthy controls (HCs). Sequential interictal scans were performed using FDG-PET and MRI. The MCI metabolic and structural brain networks were constructed according to the standardized uptake value ratio (SUVR) obtained using FDG-PET and gray matter volume obtained using MRI. The permutation test was used to compare the network parameters (characteristic path length, clustering coefficient, local efficiency, and global efficiency) between the two groups. Partial Pearson’s correlation analysis was used to calculate the correlations of the changes in gray matter volume and glucose intake in the key brain regions in MCI with the Alzheimer's Disease Assessment Scale-Cognitive (ADAS-cog) sub-item scores. Results: Significant changes in the brain network parameters (longer characteristic path length, larger clustering coefficient, and lower local efficiency and global efficiency) were greater in the structural network than in the metabolic network (longer characteristic path length) in MCI patients than in HCs. We obtained the key brain regions by scanning the hubs and found that the betweenness centrality of the right calcarine fissure and its surrounding cortex (CAL.R), left lingual gyrus (LING.L), and left globus pallidus (PAL.L) differed significantly between HCs and patients with MCI in both structural and metabolic networks (all p<0.05). The volume of gray matter atrophy in the PAL.L was significantly positively correlated with comprehension of spoken language (p=0.024) and word-finding difficulty in spontaneous speech item scores (p=0.007) in the ADAS-cog. Glucose intake in the three key brain regions (CAL.R, LING.L, and PAL.L) was significantly negatively correlated with remembering test instructions items in ADAS-cog (p=0.020, p=0.014, and p=0.008, respectively). Conclusion: MRI brain networks showed more changes than FDG-PET brain networks in patients with MCI. Some brain regions with significant changes in betweenness centrality in both structural and metabolic networks were associated with MCI.

BackgroundAbout 40–50% of patients with amnestic mild cognitive impairment (MCI) are found to have no significant Alzheimer’s pathology based on amyloid PET positivity. Notably, conversion to dementia in this population is known to occur much less often than in amyloid-positive MCI. However, the relationship between MCI and brain amyloid deposition remains largely unknown. Therefore, we investigated the influence of subthreshold levels of amyloid deposition on conversion to dementia in amnestic MCI patients with negative amyloid PET scans.MethodsThis study was a retrospective cohort study of patients with amyloid-negative amnestic MCI who visited the memory clinic of Asan Medical Center. All participants underwent detailed neuropsychological testing, brain magnetic resonance imaging, and [18F]-florbetaben (FBB) positron emission tomography scan (PET). Conversion to dementia was determined by a neurologist based on a clinical interview with a detailed neuropsychological test or a decline in the Korean version of the Mini-Mental State Examination score of more than 4 points per year combined with impaired activities of daily living. Regional cortical amyloid levels were calculated, and a receiver operating characteristic (ROC) curve for conversion to dementia was obtained. To increase the reliability of the results of the study, we analyzed the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset together.ResultsDuring the follow-up period, 36% (39/107) of patients converted to dementia from amnestic MCI. The dementia converter group displayed increased standardized uptake value ratio (SUVR) values of FBB on PET in the bilateral temporal, parietal, posterior cingulate, occipital, and left precuneus cortices as well as increased global SUVR. Among volume of interests, the left parietal SUVR predicted conversion to dementia with the highest accuracy in the ROC analysis (area under the curve [AUC] = 0.762, P < 0.001). The combination of precuneus, parietal cortex, and FBB composite SUVRs also showed a higher accuracy in predicting conversion to dementia than other models (AUC = 0.763). Of the results of ADNI data, the SUVR of the left precuneus SUVR showed the highest AUC (AUC = 0.596, P = 0.006).ConclusionOur findings suggest that subthreshold amyloid levels may contribute to conversion to dementia in patients with amyloid-negative amnestic MCI.

Similar regional anatomical distributions were reported for fibrillary amyloid deposition [measured by (11)C-Pittsburgh compound B (PIB) positron emission tomography (PET)] and brain hypometabolism [measured by (18)F-fluorodeoxyglucose (FDG) PET] in numerous Alzheimer's disease (AD) studies. However, there is a lack of longitudinal studies evaluating the interrelationships of these two different pathological markers in the same AD population. Our most recent AD study suggested that the longitudinal pattern of hypometabolism anatomically follows the pattern of amyloid deposition with temporal delay, which indicates that neuronal dysfunction may spread within the anatomical pattern of amyloid pathology. Based on this finding we now hypothesize that in early AD patients quantitative longitudinal decline in hypometabolism may be related to the amount of baseline amyloid deposition during a follow-up period of 2 years. Fifteen patients with mild probable AD underwent baseline (T1) and follow-up (T2) examination after 24 ± 2.1 months with [(18)F]FDG PET, [(11)C]PIB PET, structural T1-weighted MRI and neuropsychological testing [Consortium to Establish a Registry for Alzheimer's Disease (CERAD) neuropsychological battery]. Longitudinal cognitive measures and quantitative PET measures of amyloid deposition and metabolism [standardized uptake value ratios (SUVRs)] were obtained using volume of interest (VOI)-based approaches in the frontal-lateral-retrosplenial (FLR) network and in predefined bihemispheric brain regions after partial volume effect (PVE) correction of PET data. Statistical group comparisons (SUVRs and cognitive measures) between patients and 15 well-matched elderly controls who had undergone identical imaging procedures once as well as Pearson's correlation analyses within patients were performed. Group comparison revealed significant cognitive decline and increased mean PIB/decreased FDG SUVRs in the FLR network as well as in several AD-typical regions in patients relative to controls. Concurrent with cognitive decline patients showed longitudinal increase in mean PIB/decrease in mean FDG SUVRs over time in the FLR network and in several AD-typical brain regions. Correlation analyses of FLR network SUVRs in patients revealed significant positive correlations between PIB T1 and delta FDG (FDG T1-T2) SUVRs, between PIB T1 and PIB T2 SUVRs, between FDG T1 and PIB T2 SUVRs as well as between FDG T1 and FDG T2 SUVRs, while significant negative correlations were found between FDG T1 and delta PIB (PIB T1-T2) SUVRs as well as between FDG T2 and delta FDG (FDG T1-T2) SUVRs. These findings were confirmed in locoregional correlation analyses, revealing significant associations in the same directions for two left hemispheric regions and nine right hemispheric regions, showing the strongest association for bilateral precuneus. Baseline amyloid deposition in patients with mild probable AD was associated with longitudinal metabolic decline. Additionally, mildly decreased/relatively preserved baseline metabolism was associated with a longitudinal increase in amyloid deposition. The latter bidirectional associations were present in the whole AD-typical FLR network and in several highly interconnected hub regions (i.e. in the precuneus). Our longitudinal findings point to a bidirectional quantitative interrelationship of the two investigated AD pathologies, comprising an initial relative maintenance of neuronal activity in already amyloid-positive hub regions (neuronal compensation), followed by accelerated amyloid deposition, accompanied by functional neuronal decline (neuronal breakdown) along with cognitive decline.

Brain fluorodeoxyglucose (FDG) PET in dementia

Arterial spin labeling versus 18F-FDG-PET to identify mild cognitive impairment

Fluorodeoxyglucose (FDG) positron emission tomography (PET) is useful to predict Alzheimer's disease (AD) conversion in patients with mild cognitive impairment (MCI). However, few studies have examined the extent to which FDG PET alone can predict AD conversion and compared the efficacy between visual and computer-assisted analysis directly. The current study aimed to evaluate the value of FDG PET in predicting the conversion to AD in patients with MCI and to compare the predictive values of visual reading and computer-assisted analysis. A total of 54 patients with MCI were evaluated with FDG PET and followed-up for 2years with final diagnostic evaluation. FDG PET images were evaluated by (1) traditional visual rating, (2) composite score of visual rating of the brain cortices, and (3) composite score of computer-assisted analysis. Receiver operating characteristics (ROC) curves were compared to analyze predictive values. Nineteen patients (35.2%) converted to AD from MCI. The area under the curve (AUC) of the ROC curve of the traditional visual rating, composite score of visual rating, and computer-assisted analysis were 0.67, 0.76, and 0.79, respectively. ROC curves of the composite scores of the visual rating and computer-assisted analysis were comparable (Z = 0.463, p = 0.643). Visual rating and computer-assisted analysis of FDG PET scans were analogously accurate in predicting AD conversion in patients with MCI. Therefore, FDG PET may be a useful tool for screening AD conversion in patients with MCI, when using composite score, regardless of the method of interpretation.

Decision letter: Stage-dependent differential influence of metabolic and structural networks on memory across Alzheimer’s disease continuum

Objectives: Alzheimer's disease (AD) is the most prevalent neurodegenerative disorder and the most common form of dementia in the older people. Some types of mild cognitive impairment (MCI) are the clinical precursors of AD, while other MCI forms tend to remain stable over time and do not progress to AD. To discriminate MCI patients at risk of AD from stable MCI, we propose a novel deep-learning radiomics (DLR) model based on 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) images and combine DLR features with clinical parameters (DLR+C) to improve diagnostic performance.Methods: 18F-fluorodeoxyglucose positron emission tomography (PET) data from the Alzheimer's disease Neuroimaging Initiative database (ADNI) were collected, including 168 patients with MCI who converted to AD within 3 years and 187 patients with MCI without conversion within 3 years. These subjects were randomly partitioned into 90 % for the training/validation group and 10 % for the independent test group. The proposed DLR approach consists of three steps: base DL model pre-training, network features extraction, and integration of DLR+C, where a convolution network serves as a feature encoder, and a support vector machine (SVM) operated as the classifier. In comparative experiments, we compared our DLR+C method with four other methods: the standard uptake value ratio (SUVR) method, Radiomics-ROI method, Clinical method, and SUVR + Clinical method. To guarantee the robustness, 10-fold cross-validation was processed 100 times.Results: Under the DLR model, our proposed DLR+C was advantageous and yielded the best classification performance in the diagnosis of conversion with the accuracy, sensitivity, and specificity of 90.62 ± 1.16, 87.50 ± 0.00, and 93.39 ± 2.19%, respectively. In contrast, the respective accuracy of the other four methods reached 68.38 ± 1.27, 73.31 ± 6.93, 81.09 ± 1.97, and 85.35 ± 0.72 %. These results suggested the DLR approach could be used successfully in the prediction of conversion to AD, and that our proposed DLR-combined clinical information was effective.Conclusions: This study showed DLR+C could provide a novel and valuable method for the computer-assisted diagnosis of conversion to AD from MCI. This DLR+C method provided a quantitative biomarker which could predict conversion to AD in MCI patients.

BackgroundSpatial normalization is a prerequisite step for analyzing positron emission tomography (PET) images both by using volume-of-interest (VOI) template and voxel-based analysis. Magnetic resonance (MR) or ligand-specific PET templates are currently used for spatial normalization of PET images. We used computed tomography (CT) images acquired with PET/CT scanner for the spatial normalization for [18F]-N-3-fluoropropyl-2-betacarboxymethoxy-3-beta-(4-iodophenyl) nortropane (FP-CIT) PET images and compared target-to-cerebellar standardized uptake value ratio (SUVR) values with those obtained from MR- or PET-guided spatial normalization method in healthy controls and patients with Parkinson’s disease (PD).MethodsWe included 71 healthy controls and 56 patients with PD who underwent [18F]-FP-CIT PET scans with a PET/CT scanner and T1-weighted MR scans. Spatial normalization of MR images was done with a conventional spatial normalization tool (cvMR) and with DARTEL toolbox (dtMR) in statistical parametric mapping software. The CT images were modified in two ways, skull-stripping (ssCT) and intensity transformation (itCT). We normalized PET images with cvMR-, dtMR-, ssCT-, itCT-, and PET-guided methods by using specific templates for each modality and measured striatal SUVR with a VOI template. The SUVR values measured with FreeSurfer-generated VOIs (FSVOI) overlaid on original PET images were also used as a gold standard for comparison.ResultsThe SUVR values derived from all four structure-guided spatial normalization methods were highly correlated with those measured with FSVOI (P < 0.0001). Putaminal SUVR values were highly effective for discriminating PD patients from controls. However, the PET-guided method excessively overestimated striatal SUVR values in the PD patients by more than 30% in caudate and putamen, and thereby spoiled the linearity between the striatal SUVR values in all subjects and showed lower disease discrimination ability. Two CT-guided methods showed comparable capability with the MR-guided methods in separating PD patients from controls and showed better correlation between putaminal SUVR values and the parkinsonian motor severity than the PET-guided method.ConclusionCT-guided spatial normalization methods provided reliable striatal SUVR values comparable to those obtained with MR-guided methods. CT-guided methods can be useful for analyzing dopamine transporter PET images when MR images are unavailable.

This review article aims at providing a state-of-the-art review of the role of fluorodeoxyglucose (FDG) positron emission tomography (PET) imaging (FDG-PET) in the prediction of Alzheimer's dementia in subjects suffering mild cognitive impairment (MCI), with a particular focus on the predictive power of FDG-PET compared to structural magnetic resonance imaging (sMRI). We also address perfusion single photon emission computed tomography (SPECT) as a less costly and more accessible alternative to FDG-PET. A search in PubMed was performed, taking into consideration relevant scientific articles published in English within the last five years and limited to human studies. This recent literature confirms the effectiveness of FDG-PET and sMRI for prediction of AD dementia in MCI. However, there are discordant results regarding which image modality is superior. This could be explained by the high variability of metrics used to evaluate both imaging modalities and/or by sampling/population issues such as age, disease severity and conversion time. FDG-PET seems to outperform sMRI in rapidly converting early-onset MCI individuals, whereas sMRI may outperform FDG-PET in late-onset MCI subjects, in which case FDG PET might only provide a complementary role. Although FDG-PET performs better than perfusion SPECT, current evidence confirms perfusion SPECT as a valid alternative when FDG- PET is not available. Finally, possible future directions in the field are discussed.

Mild cognitive impairment (MCI) refers to a memory impairment among non-demented adults. It is a condition that increases the risk of dementia, notably due to Alzheimer's disease (AD). MCI is heterogeneous and there is a need for novel diagnostic approaches. Fluorodeoxyglucose positron emission tomography (FDG-PET) imaging provides robust AD biomarker characteristics, while anatomical and functional magnetic resonance imaging (MRI) offer complementary information. Classify MCI and cognitively normal (CN) adults using FDG-PET images; predict individuals with MCI that convert to AD dementia; determine if MRI can achieve comparable performance to FDG-PET classification. Four ADNI cohorts were created. Cohort 1: 805 participants (MCI n = 455; CN n = 350) that underwent FDG-PET. FDG-PET images were inputs to a one-channel 3-dimensional (3D) DenseNet deep learning model. Cohort 2: 348 participants (MCI n = 174; CN n = 174) with MRI and functional MRI. Cohort 3: overlapping cases from cohorts 1 and 2 (MCI n = 70; CN n = 70). Cohort 4: 336 participants (MCI-converters n = 168; MCI-stable n = 168) with FDG-PET from cohort 1. The one/two-channel models' inputs were T1-weighted MRI and/or amplitude of low-frequency fluctuations images, with classification metrics evaluated through 10-fold cross-validation. The FDG-PET model achieved 88.02%±3.82 accuracy for MCI versus CN classification, with 88.70%±4.70 sensitivity and 87.14%±5.03 specificity. Neither MRI model outperformed the FDG-PET model, as the highest MRI-based accuracy was 76.86%±1.95. The FDG-PET model achieved 63.23%±4.68 accuracy in classifying MCI-converters versus MCI-stable. FDG-PET images produced the highest accuracy in classifying MCI versus CN. While MRI-based approaches were inferior to FDG-PET, multi-contrast MRI still offers value for neurodegeneration classification.

BackgroundQuantitative measures of amyloid‐β (Aβ) pathology using positron emission tomography (PET) imaging are sensitive to identify pathological changes early in Alzheimer’s disease (AD). The Centiloid scale aims to standardize these in vivo amyloid quantifications to a 100‐point scale, where an average value of zero signifies high certainty of amyloid negativity and 100 identifies average typical AD Aβ‐pathology load (Klunk et al., 2015). The current study developed and validated a single and fully automated Centiloid quantification pipeline for multiple amyloid PET compounds.MethodQyScore’s® fully automated pipeline was validated on 11C‐PiB‐PET and 18F‐PET images from the Centiloid project (https://www.gaain.org/centiloid‐project): 34 young controls [age = 31.5 ± 6.3 years] and 47 AD patients [age = 67.5 ± 10.5 years; CDR = 0.5–1]. 18F tracers included Florbetapir (FBP, N = 46), Forbetaben (FBB, N = 35), Flutemetamol (FTM, N = 74) and NAV4694 (NAV, N = 55). PET/MR image pairs were both co‐registered and normalized in the MNI template space. The fully automated segmentation from QyScore®, a CE‐marked and FDA‐cleared neuroimaging medical device, parcellated the masks of the grey matter tissue (target) and of the cerebellum (reference) region (Figure 1). The standardized uptake value ratio (SUVr) was computed as the ratio of the mean signal in both regions. Correlations of 11C‐PiB‐PET and 18F‐PET SUVr values with published SUVr data were computed. Further, correlations between 18F‐PET SUVr and paired 11C‐PiB‐PET SUVr were computed. Correlation coefficients (R2) &gt; 0.7 were required to consider the Centiloid calibration valid.ResultQyScore’s® fully automated quantitative pipeline produced SUVr values well within the bounds defined by the Centiloid method (SUVr_AD‐100 = 2.08 +/‐ 0.2; SUVr_YC‐0 = 1.01+/‐ 0.05; R2 = 0.99; slope = 1.00; intercept = ‐0.44). 11C‐PiB SUVr correlation coefficients with published values were above 0.99 (Figure 2). Correlation coefficients of 18F‐PET SUVr with 11C‐PiB‐PET SUVr were respectively 0.91, 0.95, 0.96, 0.99 (Figure 3).Equations for converting to Centiloid were respectively o Centiloid = 177.79 FBP_SUVr ‐ 183.56 o Centiloid = 153.08 FBB_SUVr – 152.93 o Centiloid = 122.39 FTM_SUVr – 120.97 o Centiloid = 90.20 NAV_SUVr – 91.61ConclusionWe demonstrate the feasibility and reliability of a fully automated amyloid PET pipeline for multiple amyloid‐PET compounds (11C‐PiB and 18F) suitable for implementation in clinical trials.

BackgroundQuantitative measures of amyloid‐ß (Aß) pathology using positron emission tomography (PET) imaging are sensitive to identify pathological changes early in Alzheimer’s disease (AD). The Centiloid scale aims to standardize these in vivo amyloid quantifications to a 100‐point scale, where an average value of zero signifies high certainty of amyloid negativity and 100 identifies average typical AD Aß‐pathology load (Klunk et al., 2015). The current study developed and validated a single and fully automated Centiloid quantification pipeline for multiple amyloid PET compounds.MethodQyScore’s® fully automated pipeline was validated on 11C‐PiB‐PET and 18F‐PET images from the Centiloid project (https://www.gaain.org/centiloid‐project): 34 young controls [age = 31.5 ± 6.3 years] and 47 AD patients [age = 67.5 ± 10.5 years; CDR = 0.5–1]. 18F tracers included Florbetapir (FBP, N = 46), Forbetaben (FBB, N = 35), Flutemetamol (FTM, N = 74) and NAV4694 (NAV, N = 55). PET/MR image pairs were both co‐registered and normalized in the MNI template space. The fully automated segmentation from QyScore®, a CE‐marked and FDA‐cleared neuroimaging medical device, parcellated the masks of the grey matter tissue (target) and of the cerebellum (reference) region (Figure 1). The standardized uptake value ratio (SUVr) was computed as the ratio of the mean signal in both regions. Correlations of 11C‐PiB‐PET and 18F‐PET SUVr values with published SUVr data were computed. Further, correlations between 18F‐PET SUVr and paired 11C‐PiB‐PET SUVr were computed. Correlation coefficients (R2) &gt; 0.7 were required to consider the Centiloid calibration valid.ResultQyScore’s® fully automated quantitative pipeline produced SUVr values well within the bounds defined by the Centiloid method (SUVr_AD‐100 = 2.08 +/‐ 0.2; SUVr_YC‐0 = 1.01+/‐ 0.05; R2 = 0.99; slope = 1.00; intercept = ‐0.44). 11C‐PiB SUVr correlation coefficients with published values were above 0.99 (Figure 2). Correlation coefficients of 18F‐PET SUVr with 11C‐PiB‐PET SUVr were respectively 0.91, 0.95, 0.96, 0.99 (Figure 3). Equations for converting to Centiloid were respectively: • Centiloid = 177.79 FBP_SUVr ‐ 183.56 • Centiloid = 153.08 FBB_SUVr – 152.93 • Centiloid = 122.39 FTM_SUVr – 120.97 • Centiloid = 90.20 NAV_SUVr – 91.61ConclusionWe demonstrate the feasibility and reliability of a fully automated amyloid PET pipeline for multiple amyloid‐PET compounds (11C‐PiB and 18F) suitable for implementation in clinical trials.