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: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-01 至 2021-07-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10214874
• 关键词: Address; Affect; Age; Alzheimer' s Disease; Alzheimer' s disease pathology; American; Amyloid; Amyloid Proteins; Biological Markers; Brain; Cause of Death; Cerebrum; Clinical; Clinical Trials; Cognitive; Data; Dementia; Deposition; Diagnostic; Dose; Drowning; Early Diagnosis; Evaluation; Follow-Up Studies; Future; Hour; Image; Impaired cognition; Individual; Injections; Investigative Techniques; Longitudinal Studies; MRI Scans; Machine Learning; Magnetic Resonance Imaging; Measures; Methods; Modeling; Multimodal Imaging; Neurodegenerative Disorders; Neurofibrillary Tangles; Outcome; Participant; Pathogenesis; Patient Recruitments; Patients; Performance; Population; Positron-Emission Tomography; Proteins; Protocols documentation; Public Health; Radiation Dose Unit; Radioactivity; Reading; Research Design; Research Personnel; Risk; Running; Scanning; Schedule; Signal Transduction; Site; System; Techniques; Testing; Tracer; Training; United States; Visit; Vulnerable Populations; Work; apolipoprotein E-4; base; clinical application; convolutional neural network; cost; deep learning; deep neural network; flexibility; imaging agent; imaging study; innovation; learning network; longitudinal positron emission tomography; machine learning method; mild cognitive impairment; multimodality; neuroimaging; novel; pre-clinical; radiotracer; recruit; research study; serial imaging; sex; simulation; study population; tau Proteins; theories; uptake

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.

项目摘要 阿尔茨海默病（AD）是一种毁灭性的神经退行性疾病，是一个重大的公共卫生危机， 目前影响了580多万美国人，预计随着人口老龄化，这一数字还会上升。正电子发射 断层扫描（PET）成像可以识别AD的标志性蛋白质病变，包括淀粉样蛋白斑块 以及神经元缠结（主要由tau蛋白组成）在脑中积累。虽然有明显的 需要更多的PET神经成像，例如，以阐明淀粉样蛋白和tau蛋白沉积的顺序， 临床前AD，其在具有大研究人群的纵向成像研究中增加的效用受到以下因素的限制： 招聘和成本。特别是，对参与者来说，多次访问扫描站点将是困难的 居住距离远，以及注射放射性示踪剂的高成本将限制PET研究的可扩展性。 在这个项目中，我们建议使用基于深度学习的卷积神经网络（CNN）来增强 超低剂量淀粉样蛋白和tau PET用于AD成像。我们的具体目标是（1）验证诊断价值 在实际的超低剂量淀粉样蛋白和tau成像会话中，CNNs的注射剂量低至 原始的，并与实际的超低剂量数据，以验证模拟用于随后的目标和未来 研究;（2）将超低剂量CNN应用于其他PET系统和示踪剂上收集的数据，以便 证明CNN的普适性;（3）评估深度学习辅助超低剂量的价值 淀粉样蛋白和tau PET用于跟踪临床前AD人群中的认知下降。 这项工作的创新之处在于，除了先进的机器学习之外，还使用了多模态成像 能够以极低剂量水平采集诊断级PET图像的技术。执行 实际的超低剂量PET采集本身也是非常新颖的。这项提议的结果是， 大规模临床纵向成像的限制因素，缩短跨越多天的采集 连续超低剂量和全剂量双示踪剂扫描方案，一次就诊数小时。 还可以实现显著的剂量降低，允许更频繁的淀粉样蛋白/tau PET扫描。这 灵活性不仅将增加PET的实用性，帮助痴呆症的纵向研究，而且使未来 随着这些示踪剂的开发，多种基于PET的生物标志物的综合成像。