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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.

摘要