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QUANTIFICATION OF AMYLOID BURDEN FROM FLORBETAPIR PET IMAGES WITHOUT USING TARGET AND REFERENCE REGIONS: PRELIMINARY FINDINGS BASED ON THE DEEP LEARNING 3D CONVOLUTIONAL NEURAL NETWORK APPROACH

      Beta-amyloid (Aβ) is widely viewed as a major hallmark of Alzheimer’s disease (AD) pathology. Standard uptake value ratio (SUVr) between a pre-specified target mean-cortical region and the cerebellum (or another) reference region is the most commonly used measurement to quantify Aβ burden for amyloid positron emission tomography (PET) technique. By utilizing a 3D convolutional neural network (3D-CNN), we examined the feasibility of estimating SUVr and determining Aβ positivity from florbetapir PET images directly without defining the target and reference regions and without extracting data from them.
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