Optimization of Tau PET Imaging for Alzheimer's Disease through Deep Learning-Based Image Reconstruction

摘要 阿尔茨海默病(AD)是一种以记忆丧失、认知障碍为特征的进行性神经退行性疾病。 精神障碍和行为障碍。在美国，有620万65岁及以上的老人患有AD 美国将于2021年进入美国。AD的早期诊断具有特别重要的意义，因为治疗在 在不可逆转的脑损伤发生之前的症状前阶段。Tau神经原纤维缠结(NFT)， 在症状出现之前积累几十年，可以指示症状前阶段。根据布拉克的说法 在分期阶段，tau NFT从经鼻鼻道开始，然后在后期扩散到海马体和其他皮质。 早期检测tau NFTs并明确其模式是早期诊断和治疗的关键。 阿尔茨海默病治疗监测。随着最近在tau示踪剂开发方面的突破，正电子发射 断层扫描(PET)可以检测到tau NFTs在体内的蓄积。然而，由于信噪比(SNR)的原因 由于正电子发射计算机断层扫描的分辨率限制，很难准确恢复薄层皮质区域的tau保留模式。这是 尤其是在tau信号较弱的早期阶段。此外，最近的纵向研究表明， 正电子发射计算机断层扫描检测到的tau沉积累积变化AD组每年约为3~6%，而AD组较小 临床前AD组。这一微小的年度变化进一步挑战了当前PET的信号检测能力 系统。此外，18F-MK-6240是一种新开发的tau示踪剂，它与tau NFTs具有更高的亲和力，并且不会脱落。 早期Braak分期区域附近的靶结合，这使其在早期AD诊断中具有很高的前景。 然而，18F-MK-6240的一个问题是脑膜中的非靶向结合。考虑到它的单薄性质 皮质带及其靠近脑膜的部位，tau蓄积的定量准确性显著 妥协了。因此，在进一步提高正电子发射计算机断层扫描的分辨率和信噪比方面，仍有许多未得到满足的需求。 这项拨款申请提出了基于深度学习(DL)的图像重建方法，该方法可以提高 Tau成像的分辨率和信噪比。这项建议的四个具体目标是：(1) 开发基于动态链接库的静态PET图像重建；(2)开发基于动态链接库的动态图像重建 开发能够快速生成高质量参数图像的框架；以及(4)应用 提出了18F-MK-6240成像数据集的框架。我们期待具体目标的综合成果 将是健壮和临床有效的框架，可以生成静态和参数图像，具有改进的 静态和简化的动态tau PET成像的分辨率和信噪比。

Abstract Alzheimer’s disease (AD) is a progressive neurodegenerative disease characterized by memory loss, cognitive impairments, and behavioral disorders. 6.2 million people aged 65 and older are living with AD in the United States in 2021. Earlier diagnosis of AD holds particular significance as therapies are most effective during the pre-symptomatic stages before irreversible brain damage has occurred. Tau neurofibrillary tangles (NFTs), accumulating decades before symptomatic onset, can indicate the pre-symptomatic stages. According to Braak staging, tau NFTs start from transentorhinal, then spreading to hippocampus and other cortices at later stages. Detecting tau NFTs during early stages and clearly resolving their patterns is essential for early diagnosis and treatment monitoring of AD. With recent breakthroughs in tau tracer developments, Positron Emission Tomography (PET) can detect accumulation of tau NFTs in vivo. However, due to signal-to-noise ratio (SNR) and resolution limits of PET, accurate recovery of tau retention patterns in thin cortical regions is difficult. This is especially true for early stages when tau signal is weak. Additionally, recent longitudinal studies show that the accumulation change of tau deposits detected by PET is around 3 to 6 % per year for the AD group, and less for the preclinical AD group. This small annual change further challenges the signal detectability of current PET systems. Furthermore, 18F-MK-6240 is a newly developed tau tracer with higher affinity to tau NFTs and no off- target bindings near early Braak-staging regions, which makes it highly promising for early AD diagnosis. However, one issue with 18F-MK-6240 is the off-target bindings in the meninges. Given the thin nature of the cortical ribbon and its proximity to the meninges, quantitative accuracy of tau accumulation is significantly compromised. Consequently, there are unmet needs to further improve PET resolution and SNR for tau imaging. This grant application proposes deep learning (DL)-based image reconstruction methods that can improve the resolution and signal-to-noise ratio (SNR) of tau imaging. The four specific aims of this proposal are (1) to develop DL-based static PET image reconstruction; (2) to develop DL-based image reconstruction for dynamic PET; (3) to develop frameworks that can rapidly produce high-quality parametric images; and (4) to apply the proposed frameworks to 18F-MK-6240 imaging datasets. We expect the integrated outcome of the specific aims will be robust and clinically effective frameworks that can generate static and parametric images with improved resolution and SNR from static and simplified dynamic tau PET imaging.