Improved separation of Alzheimer’s disease and related disorders using dual-point amyloid-PET

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F-Florbetaben (FBB) is an amyloid radiotracer that is being increasingly used to assist the diagnosis of Alzheimers disease (AD). Recent studies have suggested that early acquisitions (immediately after the radiotracer injection) of
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F-FBB data provide information about the neuronal injury. Although this assumption still needs to be corroborated by further studies with larger datasets, this would allow obtaining information about the brain injury and amyloid deposits in single examination, using a dual-point protocol. Despite previous works are focused on analyzing the equivalence between early
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F-FBB-PET and
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F-FDG-PET images, in this work we analyzed whether the information provided by early and standard
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F-FBB-PET acquisitions is complementary and can be used together to improve the automatic separation of AD and non-AD patients. Two approaches were proposed to combine the two images from each patient into a single observation: feature concatenation and multiple kernel learning. Then, a Support Vector Machine classifier was used to separate the groups. The classification performance was estimated using a dataset with
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F-FBB-PET data from 80 patients (44 AD and 36 non-AD) along with a cross-validation scheme. Accuracy, sensitivity and specificity achieved by the proposed approaches were compared to those obtained by approaches using only data from one acquisition. The results suggest that using both
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F-FBB-PET acquisitions improves the automatic separation of AD and non-AD patients. They also corroborate that early 18F-FBB-PET images contains useful information (different to that contained in standard
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F-FBB-PET images) to distinguish between AD and non-AD patients. That would make early
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F-FBB-PET images an affordable way to obtain additional information from amyloid tracers that would result in more accurate diagnosis.