AI-Enhanced Brain PET Imaging for Alzheimer's Disease

AI 增强型大脑 PET 成像治疗阿尔茨海默病

基本信息

批准号：
10670483
负责人：
Gregory George Zaharchuk
金额：
$ 77.82万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-09-01 至 2024-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10670483
关键词：
Alzheimer&apos s Disease Alzheimer&apos s disease risk Alzheimer’s disease biomarker Amyloid Artificial Intelligence Binding Blood flow Brain Clinical Research Clinical Trials Cognition Disorders Cognitive Country Dementia Discipline of Nuclear Medicine Disease Disease Progression Dose Evaluation Evaluation Research Functional disorder Future Goals Health Human Image Imaging Techniques Individual Lead Logistics Machine Learning Magnetic Resonance Imaging Medical Imaging Memory Metabolism Methods Molecular Molecular Target Neurofibrillary Tangles Patient-Focused Outcomes Patients Pattern Positron-Emission Tomography Prevention trial Process Radiation Dose Unit Research Risk Solid Spatial Distribution Synapses Techniques Testing Time Tracer Validation Voice Water amyloid imaging analog apolipoprotein E-4 attenuation cohort contrast imaging convolutional neural network cost deep learning dementia care dementia risk density evidence based guidelines follow-up high risk imaging modality imaging study improved in vivo in vivo imaging insight molecular imaging multimodality novel polygenic risk score radioligand radiotracer side effect simulation tau Proteins volunteer

项目摘要

Abstract Brain PET imaging has fundamentally advanced our understanding of the pathophysiology and progression of Alzheimer's disease (AD). However, the full value of this imaging modality is not realized due to issues around radiotracer dose, cost, and logistics. We believe artificial intelligence (AI) derived methods can significantly impact PET imaging in a way that makes it much more useful for molecular imaging of dementia. The goal of this project is to develop and test convolutional neural network approaches to improve the quality of brain PET imaging for two commonly used radiotracers (amyloid and tau) for dementia. We will use this to significantly reduce radiotracer dose by several orders of magnitude, freeing up PET for use in non-elderly cohorts and at repeated timepoints to better understand the early presence and subsequent progression of disease. We will also show how this new capability can enable novel paradigms for multi-tracer, single-session PET imaging. Successful completion of this study will result in validation of AI techniques for ultra-low dose PET imaging and a demonstration of how this capability can be harnessed to improve future brain molecular imaging studies to better understand the pathophysiology of AD. As such, it will provide solid, evidence- based recommendations for future clinical and research evaluation in patients with or at high risk of dementia.

抽象的脑 PET 成像从根本上增进了我们对阿尔茨海默病（AD）的病理生理学和进展。然而，由于以下问题，这种成像方式的全部价值尚未实现放射性示踪剂剂量、成本和物流。我们相信人工智能（AI）派生方法可以显着影响 PET 成像，从而使其对于痴呆症的分子成像更有用。该项目的目标是开发和测试卷积神经网络提高两种常见脑部 PET 成像质量的方法使用放射性示踪剂（淀粉样蛋白和 tau 蛋白）治疗痴呆症。我们将用它来显着减少放射性示踪剂剂量几个数量级，从而释放 PET 用于非老年人群并在重复的时间点上使用，以更好地了解疾病的早期存在和随后的进展。我们将还展示了这种新功能如何为多追踪器提供新颖的范式，单次 PET 成像。这项研究的成功完成将验证人工智能技术的有效性超低剂量 PET 成像以及如何实现此功能的演示利用它来改进未来的脑分子成像研究，以更好地了解 AD 的病理生理学。因此，它将提供确凿的证据—— 基于对患者未来临床和研究评估的建议患有痴呆症或患有痴呆症的高风险。