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

摘要 阿尔茨海默病（Alzheimer's disease，AD）是一种进行性神经退行性疾病，其特征在于记忆丧失、认知功能障碍、 损伤和行为障碍。6.2在美国，有100万65岁及以上的人患有AD， 2021年的国家。AD的早期诊断具有特别重要的意义，因为治疗在治疗期间最有效。 不可逆脑损伤发生前的症状前阶段。Tau神经元缠结（NFT）， 在症状发作前数十年累积，可以指示症状前阶段。根据Braak 在阶段中，tau NFT从经内嗅开始，然后在后期扩散到海马和其他皮质。 在早期阶段检测tau NFT并清楚地解析其模式对于早期诊断和治疗至关重要。 AD的治疗监测。随着tau示踪剂开发的最新突破，正电子发射 断层扫描（PET）可以检测tau NFT在体内的积累。然而，由于信噪比（SNR） 和PET的分辨率限制，很难准确恢复薄皮质区域中的tau保留模式。这是 对于tau信号弱的早期阶段尤其如此。此外，最近的纵向研究表明， 对于AD组，通过PET检测到的tau沉积物的累积变化为每年约3%至6%，而对于AD组则更少。 临床前AD组。这一微小的年度变化进一步挑战了当前PET的信号可检测性 系统.此外，18F-MK-6240是一种新开发的tau示踪剂，对tau NFT具有更高的亲和力，并且没有脱落。 在早期Braak分期区域附近的靶向结合，这使得它非常有希望用于早期AD诊断。 然而，18F-MK-6240的一个问题是脑膜中的脱靶结合。由于薄的性质， 皮质带及其与脑膜的接近程度，tau积累的定量准确性显著降低。 暴露了因此，存在进一步提高用于tau成像的PET分辨率和SNR的未满足的需求。 这项拨款申请提出了基于深度学习（DL）的图像重建方法，可以改善 分辨率和信噪比（SNR）。这项建议的四个具体目标是：（1） 开发基于DL的静态PET图像重建;（2）开发基于DL的动态PET图像重建 PET;（3）开发可以快速生成高质量参数图像的框架;以及（4）应用 18F-MK-6240成像数据集的拟议框架。我们期待具体目标的综合成果 将是稳健且临床有效的框架，可以生成静态和参数图像， 静态和简化动态tau PET成像的分辨率和SNR。

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.