AI-Enhanced Brain PET Imaging for Alzheimer's Disease

摘要 脑PET成像从根本上提高了我们对 阿尔茨海默病(AD)的病理生理和进展。然而， 由于以下方面的问题，此成像模式的全部价值尚未实现 放射性示踪剂剂量、成本和后勤。我们相信人工智能(AI) 派生方法可以显著影响PET成像，使其 对痴呆症的分子成像更有用。 本项目的目标是开发和测试卷积神经网络。 提高两种常见脑部正电子发射计算机断层显像质量的方法 使用放射性示踪剂(淀粉样蛋白和tau)治疗痴呆症。我们将利用这一点 显著减少几个数量级的放射性示踪剂剂量，释放出 用于非老年人群的宠物，并在重复的时间点上更好地 了解疾病的早期存在和随后的发展。我们会 还展示了这一新能力如何实现多示踪剂的新范例， 单程正电子发射计算机断层扫描。 这项研究的成功完成将导致对人工智能技术的验证 超低剂量PET成像以及这种能力如何实现的演示 利用来改进未来的大脑分子成像研究，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 患有痴呆症或有患痴呆症的高危人群。

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.