BACKGROUND: Beta amyloid in the brain, which was originally confirmed by post-mortem examinations, can now be confirmed in living patients using amyloid positron emission tomography (PET) tracers, and the accuracy of diagnosis can be improved by beta amyloid plaque confirmation in patients. Amyloid deposition in the brain is often associated with the expression of dementia. Hence, it is important to identify the anatomically and functionally meaningful areas of the human brain cortex surface using PET to diagnose the possibility of developing dementia. In this study, we demonstrated the validity of automated 18F-flutemetamol PET lesion detection and segmentation based on a complete 2D U-Net convolutional neural network via masking treatment strategies.
METHODS: PET data were first normalized by volume and divided into five amyloid accumulation zones through axial, coronary, and thalamic slices. A single U-Net was trained using a divided dataset for one of these zones. Ground truth segmentations were obtained by manual delineation and thresholding (1.5 × background).
RESULTS: The following intersection over union values were obtained for the various slices in the verification dataset: frontal lobe axial/sagittal: 0.733/0.804; posterior cingulate cortex and precuneus coronal/sagittal: 0.661/0.726; lateral temporal lobe axial/coronal: 0.864/0.892; parietal lobe axial/coronal: 0.542/0.759; and striatum axial/sagittal: 0.679/0.752. The U-Net convolutional neural network architecture allowed fully automated 2D division of the 18F-flutemetamol PET brain images of Alzheimer\'s patients.
CONCLUSIONS: As dementia should be tested and evaluated in various ways, there is a need for artificial intelligence programs. This study can serve as a reference for future studies using auxiliary roles and research in Alzheimer\'s diagnosis.

BACKGROUND: Amyloid-β protein (Aβ) is one of the biomarkers for Alzheimer\'s disease (AD). The recent application of interhemispheric functional connectivity (IFC) in resting-state fMRI has been used as a non-invasive diagnostic tool for early dementia. In this study, we focused on the level of Aβ accumulated and its effects on the major functional networks, including default mode network (DMN), central executive network (CEN), salience network (SN), self-referential network (SRN) and sensory motor network (SMN).
METHODS: 58 participants (27 Hi Aβ (HiAmy) and 31 low Aβ (LowAmy)) and 25 healthy controls (HC) were recruited. [18F]flutemetamol PET/CT was performed for diseased groups, and MRI scanning was done for all participants. Voxel-by-voxel correlation analysis was done for both groups in all networks.
RESULTS: In HiAmy, IFC was reduced in all networks except SN. A negative correlation in DMN, CEN, SRN and SMN suggests high Aβ related to IFC reduction; However, a positive correlation in SN suggests high Aβ related to an increase in IFC. In LowAmy, IFC increased in CEN, SMN, SN and SRN. Positive correlation in all major brain networks.
CONCLUSIONS: The level of Aβ accumulated demonstrated differential effects on IFC in various brain networks. As the treatment to reduce Aβ plaque deposition is available in the market, it may be an option for the HiAmy group to improve their IFC in major brain networks.

End-of-life studies have validated the binary visual reads of 18F-labeled amyloid PET tracers as an accurate tool for the presence or absence of increased neuritic amyloid plaque density. In this study, the performance of a support vector machine (SVM)-based classifier will be tested against pathological ground truths and its performance determined in cognitively healthy older adults.
We applied SVM with a linear kernel to an 18F-Flutemetamol end-of-life dataset to determine the regions with the highest feature weights in a data-driven manner and to compare between two different pathological ground truths: based on neuritic amyloid plaque density or on amyloid phases, respectively. We also trained and tested classifiers based on the 10% voxels with the highest amplitudes of feature weights for each of the two neuropathological ground truths. Next, we tested the classifiers\' diagnostic performance in the asymptomatic Alzheimer\'s disease (AD) phase, a phase of interest for future drug development, in an independent dataset of cognitively intact older adults, the Flemish Prevent AD Cohort-KU Leuven (F-PACK). A regression analysis was conducted between the Centiloid (CL) value in a composite volume of interest (VOI), as index for amyloid load, and the distance to the hyperplane for each of the two classifiers, based on the two pathological ground truths. A receiver operating characteristic analysis was also performed to determine the CL threshold that optimally discriminates between neuritic amyloid plaque positivity versus negativity, or amyloid phase positivity versus negativity, within F-PACK.
The classifiers yielded adequate specificity and sensitivity within the end-of-life dataset (neuritic amyloid plaque density classifier: specificity of 90.2% and sensitivity of 83.7%; amyloid phase classifier: specificity of 98.4% and sensitivity of 84.0%). The regions with the highest feature weights corresponded to precuneus, caudate, anteromedial prefrontal, and also posterior inferior temporal and inferior parietal cortex. In the cognitively normal cohort, the correlation coefficient between CL and distance to the hyperplane was -0.66 for the classifier trained with neuritic amyloid plaque density, and -0.88 for the classifier trained with amyloid phases. This difference was significant. The optimal CL cut-off for discriminating positive versus negative scans was CL = 48-51 for the different classifiers (area under the curve (AUC) = 99.9%), except for the classifier trained with amyloid phases and based on the 10% voxels with highest feature weights. There the cut-off was CL = 26 (AUC = 99.5%), which closely matched the CL threshold for discriminating phases 0-2 from 3-5 based on the end-of-life dataset and the neuropathological ground truth.
Among a set of neuropathologically validated classifiers trained with end-of-life cases, transfer to a cognitively normal population works best for a classifier trained with amyloid phases and using only voxels with the highest amplitudes of feature weights.

UNASSIGNED: The aim was to study whether the computed tomography (CT) density and β-amyloid (Aβ) level of intraorbital optic nerve could assist in diagnosing mild cognitive impairment (MCI) and Alzheimer\'s disease (AD).
UNASSIGNED: A total of sixty subjects were recruited in our study, including nine normal control (NC) subjects (i.e., 4 men and 5 women), twenty four MCI subjects (i.e., 11 men and 13 women), and twenty seven AD subjects (i.e., 14 men and 13 women). All subjects conducted 18F-flutemetamol amyloid positron emission tomography (PET)/CT imaging. Blinded to the clinical information of the subjects, two physicians independently measured and calculated the standardized uptake value ratio (SUVR) of the bilateral occipital cortex, SUVR of the bilateral intraorbital optic nerve, and CT density of the bilateral intraorbital optic nerve by using GE AW 4.5 Workstation.
UNASSIGNED: Between AD and NC groups, the differences of the bilateral intraorbital optic nerve SUVR were statistically significant; between AD and MCI groups, the differences of the left intraorbital optic nerve SUVR were statistically significant. Between any two of the three groups, the differences in the bilateral intraorbital optic nerve density were statistically significant. The bilateral occipital SUVR was positively correlated with the bilateral intraorbital optic nerve SUVR and negatively correlated with the bilateral intraorbital optic nerve density. Bilateral intraorbital optic nerve SUVR was negatively correlated with the bilateral intraorbital optic nerve density. The area under the receiver operating characteristic (ROC) curve of multiple logistic regression was 0.9167 (for MCI vs. NC) and 0.8951 (for AD vs. MCI). The Montreal Cognitive Assessment (MoCA) and Mini-Mental State Examination (MMSE) scores were positively associated with the intraorbital optic nerve density and were negatively associated with the intraorbital optic nerve SUVR. The regression equation of MoCA was y = 16.37-0.9734 × x 1 + 0.5642 × x 2-3.127 × x 3 + 0.0275 × x 4; the R 2 was 0.848. The regression equation of MMSE was y = 19.57-1.633 × x 1 + 0.4397 × x 2-1.713 × x 3 + 0.0424 × x 4; the R 2 was 0.827.
UNASSIGNED: The CT density and Aβ deposition of the intraorbital optic nerve were associated with Aβ deposition of the occipital cortex and the severity of cognitive impairment. The intraorbital optic nerve CT density and intraorbital optic nerve Aβ deposition could assist in diagnosing MCI and AD.

PET imaging with β-amyloid ligands is emerging as a molecular imaging technique targeting white matter integrity and demyelination. β-amyloid PET ligands such as 11C-Pittsburgh compound B (11C-PiB) have been considered for quantitative measurement of myelin content changes in multiple sclerosis, but 11C-PiB is not commercially available given its short half-life. A 18F PET ligand such as flutemetamol with a longer half-life may be an alternative, but its ability to differentiate white matter hyperintensities (WMH) from normal-appearing white matter (NAWM) and its relationship with age remains to be investigated. Methods: Cognitively unimpaired (CU) older and younger adults (n = 61) were recruited from the community responding to a study advertisement for β-amyloid PET. Participants prospectively underwent MRI, 11C-PiB, and 18F-flutemetamol PET scans. MRI fluid-attenuated inversion recovery images were segmented into WMH and NAWM and registered to the T1-weighted MRI. 11C-PiB and 18F-flutemetamol PET images were also registered to the T1-weighted MRI. 11C-PiB and 18F-flutemetamol SUV ratios (SUVrs) from the WMH and NAWM were calculated using cerebellar crus uptake as a reference for both 11C-PiB and 18F-flutemetamol. Results: The median age was 38 y (range, 30-48 y) in younger adults and 67 y (range, 61-83 y) in older adults. WMH and NAWM SUVrs were higher with 18F-flutemetamol than with 11C-PiB in both older (P < 0.001) and younger (P < 0.001) CU adults. 11C-PiB and 18F-flutemetamol SUVrs were higher in older than in younger CU adults in both WMH (P < 0.001) and NAWM (P < 0.001). 11C-PiB and 18F-flutemetamol SUVrs were higher in NAWM than WMH in both older (P < 0.001) and younger (P < 0.001) CU adults. There was no apparent difference between 11C-PiB and 18F-flutemetamol SUVrs in differentiating WMH from NAWM in older and in younger adults. Conclusion:11C-PiB and 18F-flutemetamol show a similar topographic pattern of uptake in white matter with a similar association with age in WMH and NAWM. 11C-PiB and 18F-flutemetamol can also effectively distinguish between WMH and NAWM. However, given its longer half-life, commercial availability, and higher binding potential, 18F-flutemetamol can be an alternative to 11C-PiB in molecular imaging studies specifically targeting multiple sclerosis to evaluate white matter integrity.

OBJECTIVE: The recommended start time for 18F-flutemetamol amyloid positron emission tomography (PET) examination is 60-120 min after 18F-flutemetamol injection, while an acquisition time of 10-30 min is generally recommended. We aimed to elucidate the effects of different examination conditions on image quality, diagnostic ability, and quantitative value of amyloid PET using 18F-flutemetamol.
METHODS: We acquired data on a Discovery PET/computed tomography 710 scanner using Hoffman brain and pillar phantoms with 20 MBq of 18F for 30 min. The images were reconstructed into 10-, 20-, and 30-min periods. The ordered subset-expectation maximization algorithm was used for image reconstruction, which uses a 2- or 4-mm Gaussian filter and a combination of iteration and subset numbers. The percentage contrast and coefficient of variation (CV; as the image noise) were used as physical evaluation indices for reconstructed images, and images with superior contrast and low image noise were selected for clinical evaluation. The imaging data of 15 symptomatic patients (n = 7 and n = 8 for positive and negative diagnoses of Alzheimer\'s disease, respectively) were reconstructed under the phantom study conditions. Radiographers visually evaluated and ranked the clinical images based on the overall contrast and image noise, and nuclear medicine specialists diagnosed Alzheimer\'s disease. We compared the standardized uptake value ratio (SUVR) obtained with different acquisition conditions.
RESULTS: The basic study using the phantom revealed high convergence of contrast and image noise in five patterns of acquisition time and filter strengths. Regarding visual evaluation, the use of a 2-mm Gaussian filter caused difficulties in diagnosis because the brain parenchymal accumulation was mottled with high image noise. Differences in image quality and diagnostic ability due to different examination times were not significant. Differences in the SUVR were not significant in patients with a negative Alzheimer\'s disease diagnosis; in patients with a positive diagnosis, the SUVR showed significant fluctuation depending on the acquisition conditions.
CONCLUSIONS: The differences in image quality and diagnostic performance due to the differences in 10-min acquisition time were not significant; however, of note, SUVR showed significant fluctuation depending on the acquisition conditions in patients diagnosed with Alzheimer\'s disease.

Background: In clinical practice, equivocal findings are inevitable in visual interpretation of whether amyloid positron emission tomography (PET) is positive or negative. It is therefore necessary to establish a more objective quantitative evaluation method for determining the indication for disease-modifying drugs currently under development. Aims: We aimed to determine cutoffs for positivity in quantitative analysis of 18F-flutemetamol PET in patients with cognitive impairment and suspected Alzheimer\'s disease (AD). We also evaluated the clinical efficacy of amyloid PET in the diagnosis of AD. This study was registered in the Japan Registry of Clinical Trials (jRCTs, 031180321). Methods: Ninety-three patients suspected of having AD underwent 18F-flutemetamol PET in seven institutions. A PET image for each patient was visually assessed and dichotomously rated as either amyloid-positive or amyloid-negative by two board-certified nuclear medicine physicians. If the two readers obtained different interpretations, the visual rating was rerun until they reached consensus. The PET images were quantitatively analyzed using the standardized uptake value ratio (SUVR) and standardized Centiloid (CL) scale with the whole cerebellum as a reference area. Results: Visual interpretation obtained 61 positive and 32 negative PET scans. Receiver operating characteristic analysis determined the best agreement of quantitative assessments and visual interpretation of PET scans to have an area under curve of 0.982 at an SUVR of 1.13 and a CL of 16. Using these cutoff values, there was high agreement between the two approaches (kappa = 0.88). Five discordant cases had SUVR and CL values ranging from 1.00 to 1.22 and from 1 to 26, respectively. In these discordant cases, either diffuse or mildly focal elevation of cortical activity confused visual interpretation. The amyloid PET outcome significantly altered the diagnosis of AD (χ2 = 51.3, p < 0.0001). PET imaging elevated the proportions of the very high likelihood category from 20.4 to 46.2% and the very low likelihood category from 0 to 22.6%. Conclusion: Quantitative analysis of amyloid PET using 18F-flutemetamol can objectively evaluate amyloid positivity using the determined cutoffs for SUVR and CL. Moreover, amyloid PET may have added value over the standard diagnostic workup in dementia patients with cognitive impairment and suspected AD.

With the more widespread use of 18F-radioligand-based amyloid-β (Aβ) PET-CT imaging, we evaluated Aβ binding and the utility of neocortical 18F-Flutemetamol standardized uptake value ratio (SUVR) as a biomarker.
18F-Flutemetamol SUVR was used to differentiate 1) mild cognitive impairment (MCI) from Alzheimer\'s disease (AD), and 2) MCI from other non-AD dementias (OD).
109 patients consecutively recruited from a University memory clinic underwent clinical evaluation, neuropsychological test, MRI and 18F-Flutemetamol PET-CT. The diagnosis was made by consensus of a panel consisting of 1 neuroradiologist and 2 geriatricians. The final cohort included 13 subjective cognitive decline (SCD), 22 AD, 39 MCI, and 35 OD. Quantitative analysis of 16 region-of-interests made by Cortex ID software (GE Healthcare).
The global mean 18F-Flutemetamol SUVR in SCD, MCI, AD, and OD were 0.50 (SD-0.08), 0.53 (SD-0.16), 0.76 (SD-0.10), and 0.56 (SD-0.16), respectively, with SUVR in SCD and MCI and OD being significantly lower than AD. Aβ binding in SCD, MCI, and OD was heterogeneous, being 23%, 38.5%, and 42.9% respectively, as compared to 100% amyloid positivity in AD. Using global SUVR, ROC analysis showed AUC of 0.868 and 0.588 in differentiating MCI from AD and MCI from OD respectively.
18F-Flutemetamol SUVR differentiated MCI from AD with high efficacy (high negative predictive value), but much lower efficacy from OD. The major benefit of the test was to differentiate cognitively impaired patients (either SCD, MCI, or OD) without AD-related-amyloid-pathology from AD in the clinical setting, which was under-emphasized in the current guidelines proposed by Amyloid Imaging Task Force.

18F-florbetaben (FBB) and 18F-flutemetamol (FMM) amyloid PET have been developed and approved for clinical use. It is important to understand the distinct features of these ligands to compare and correctly interpret the results of different amyloid PET studies.
We performed a head-to-head comparison of FBB and FMM to compare with regard to imaging characteristics, including dynamic range of retention, and differences in quantitative measurements between the two ligands in cortical, striatal, and white matter (WM) regions.
Paired FBB and FMM PET images were acquired in 107 participants. Correlations of FBB and FMM amyloid deposition in the cortex, striatum, and WM were investigated and compared in different reference regions (cerebellar gray matter (CG), whole cerebellum (WC), WC with brainstem (WC + B), and pons).
The cortical SUVR (R2 = 0.97) and striatal SUVR (R2 = 0.95) demonstrated an excellent linear correlation between FBB and FMM using a WC as reference region. There was no difference in the cortical SUVR ratio between the two ligands (p = 0.90), but the striatal SUVR ratio was higher in FMM than in FBB (p < 0.001). Also, the effect size of differences in striatal SUVR seemed to be higher with FMM (2.61) than with FBB (2.34). These trends were similarly observed according to four different reference regions (CG, WC, WC + B, and pons).
Our findings suggest that FMM might be better than FBB to detect amyloid burden in the striatum, although both ligands are comparable for imaging AD pathology in vivo.

BACKGROUND: Positron emission tomography (PET) is increasingly being used as an imaging modality for clinical and research applications in veterinary medicine. Amyloid PET has become a useful tool for diagnosing Alzheimer\'s disease (AD) in humans, by accurately identifying amyloid-beta (Aβ) plaques. Cognitive dysfunction syndrome in dogs shows cognitive and pathophysiologic characteristics similar to AD. Therefore, we assessed the physiologic characteristics of uptake of 18F-flutemetamol, an Aβ protein-binding PET tracer in clinical development, in normal dog brains, for distinguishing an abnormal state. Static and dynamic PET images of six adult healthy dogs were acquired after 18F-flutemetamol was administered intravenously at approximately 3.083 MBq/kg. For static images, PET data were acquired at 30, 60, and 90 min after injection. One week later, dynamic images were acquired for 120 min, from the time of tracer injection. PET data were reconstructed using an iterative technique, and corrections for attenuation and scatter were applied. Regions of interest were manually drawn over the frontal, parietal, temporal, occipital, anterior cingulate, posterior cingulate, and cerebellar cortices, cerebral white matter, midbrain, pons, and medulla oblongata. After calculating standardized uptake values with an established formula, standardized uptake value ratios (SUVRs) were obtained, using the cerebellar cortex as a reference region.
RESULTS: Among the six cerebral cortical regions, the cingulate cortices and frontal lobe showed the highest SUVRs. The lowest SUVR was observed in the occipital lobe. The average values of the cortical SUVRs were 1.25, 1.26, and 1.27 at 30, 60, and 90 min post-injection, respectively. Tracer uptake on dynamic scans was rapid, peaking within 4 min post-injection. After reaching this early maximum, cerebral cortical regions showed a curve with a steep descent, whereas cerebral white matter demonstrated a curve with a slow decline, resulting in a large gap between cerebral cortical regions and white matter.
CONCLUSIONS: This study provides normal baseline data of 18F-flutemetamol PET that can facilitate an objective diagnosis of cognitive dysfunction syndrome in dogs in future.