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Generalizable Deep Learning Networks for Dual-tracer Amyloid/Tau PET/MRI Imaging of Alzheimer's Disease

Generalizable Deep Learning Networks for Dual-tracer Amyloid/Tau PET/MRI Imaging of Alzheimer's Disease
用于阿尔茨海默病双示踪剂淀粉样蛋白/Tau PET/MRI 成像的可推广深度学习网络
批准号:
10214874
负责人:
Kevin Tze-Hsiang Chen
金额:
$10.09万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2021
资助国家:
美国
项目状态:
已结题
起止时间:
2021-05-01 至 2021-07-30

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Project Summary Alzheimer’s Disease (AD) is a devastating neurodegenerative disorder and a major public health crisis, currently affecting over 5.8 million Americans and expected to rise as the population ages. Positron emission tomography (PET) imaging can identify the hallmark proteinopathies of AD, including amyloid protein plaques and neurofibrillary tangles (composed primarily of tau protein) accumulating in the brain. While there is evident need for more PET neuroimaging, for example, to elucidate the sequence of amyloid and tau deposition in preclinical AD, its increased utility in longitudinal imaging studies with large study populations is limited by recruitment and cost. In particular, making multiple visits to the scanning site will be difficult for participants living far away, and the high cost of injected radiotracers will limit the scalability of PET studies. In this project we propose using deep learning-based convolutional neural networks (CNNs) to enhance ultra-low-dose amyloid and tau PET for imaging AD. Our specific aims are (1) to validate the diagnostic value of the CNNs in actual ultra-low-dose amyloid and tau imaging sessions, with the injected dose as low as 1% of the original, and with actual ultra-low-dose data, to validate simulations for use in subsequent aims and future studies; (2) to apply the ultra-low-dose CNN to data collected on other PET systems and tracers, in order to demonstrate the CNN’s generalizability; and (3) to evaluate the value of deep learning-aided ultra-low-dose amyloid and tau PET for tracking cognitive decline in a preclinical AD population. The innovation of this work lies in using multimodal imaging in addition to advanced machine learning techniques to enable acquisition of diagnostic-level PET images at extremely low dose levels. Performing actual ultra-low-dose PET acquisitions is also highly novel in itself. The outcome of this proposal is removing the limiting factors to large-scale clinical longitudinal imaging, shortening acquisitions spanning multiple days and visits to several hours in one visit with a successive ultra-low-dose and full-dose dual-tracer scan protocol. Significant dose reduction can also be achieved, allowing for more frequent amyloid/tau PET scanning. This flexibility will not only increase the utility of PET, aid longitudinal studies in dementia, but enable future comprehensive imaging of multiple PET-based biomarkers as these tracers are being developed.
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