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.

抽象的脑宠物成像从根本上提出了我们对阿尔茨海默氏病的病理生理学和进展（AD）。但是，由于周围的问题，无法实现这种成像方式的全部价值放射性示意剂剂量，成本和物流。我们相信人工智能（AI）派生的方法可以显着影响宠物成像，以使其成为对于痴呆的分子成像更有用。该项目的目的是开发和测试卷积神经网络提高两个通常两种脑宠物成像质量的方法使用的放射性示例（淀粉样蛋白和TAU）用于痴呆。我们将使用它显着将放射性示例剂量降低了几个数量级，释放 PET用于非善良队列，并在重复的时间点上使用以更好了解疾病的早期存在和随后的进展。我们将还显示这种新功能如何为多迹象启用新颖的范式，单会宠物成像。这项研究的成功完成将导致对AI技术的验证超低剂量宠物成像，并演示了这种能力如何利用以改善未来的大脑分子成像研究以更好了解AD的病理生理学。因此，它将提供可靠的证据 - 基于对患者的未来临床和研究评估的建议痴呆症风险或高风险。