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KMID : 1148920230570020061
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Nuclear Medicine and Molecular Imaging
2023 Volume.57 No. 2 p.61 ~ p.72
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Alzheimer’s Disease Prediction Using Attention Mechanism with Dual-Phase 18F-Florbetaben Images
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Kang Hyeon
Kang Do-Young
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Abstract
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Introduction : Amyloid-beta (Aβ) imaging test plays an important role in the early diagnosis and research of biomarkers of Alzheimer’s disease (AD) but a single test may produce Aβ-negative AD or Aβ-positive cognitively normal (CN). In this study, we aimed to distinguish AD from CN with dual-phase 18F-Florbetaben (FBB) via a deep learning?based attention method and evaluate the AD positivity scores compared to late-phase FBB which is currently adopted for AD diagnosis.
Materials and Methods : A total of 264 patients (74 CN and 190 AD), who underwent FBB imaging test and neuropsychological tests, were retrospectively analyzed. Early- and delay-phase FBB images were spatially normalized with an in-house FBB template. The regional standard uptake value ratios were calculated with the cerebellar region as a reference region and used as independent variables that predict the diagnostic label assigned to the raw image.
Results : AD positivity scores estimated from dual-phase FBB showed better accuracy (ACC) and area under the receiver operating characteristic curve (AUROC) for AD detection (ACC: 0.858, AUROC: 0.831) than those from delay phase FBB imaging (ACC: 0.821, AUROC: 0.794). AD positivity score estimated by dual-phase FBB (R: ?0.5412) shows a higher correlation with psychological test compared to only dFBB (R: ?0.2975). In the relevance analysis, we observed that LSTM uses different time and regions of early-phase FBB for each disease group for AD detection.
Conclusions : These results show that the aggregated model with dual-phase FBB with long short-term memory and attention mechanism can be used to provide a more accurate AD positivity score, which shows a closer association with AD, than the prediction with only a single phase FBB.
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KEYWORD
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Alzheimer’s disease, Amyloid-β, Blood perfusion, Functional neuroimaging, Machine learning, Neural network
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