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

通过基于深度学习的图像重建优化阿尔茨海默病的 Tau PET 成像

• 批准号: 10501804
• 负责人: Kuang Gong
• 金额: $ 48.06万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2027-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10501804
• 关键词: Address; Adoption; Aducanumab; Affinity; Alzheimer' s Disease; Alzheimer' s disease diagnosis; Applications Grants; Behavior Disorders; Binding; Biological Markers; Brain Injuries; Brain region; Cerebrovascular Circulation; Clinical; Clinical Trials; Cognitive; Data; Data Set; Deposition; Development; Diagnosis; Early Diagnosis; Early treatment; FDA approved; Family; Generations; Goals; Hippocampus (Brain); Hour; Image; Impaired cognition; Kinetics; Label; Longitudinal Studies; Measures; Memory Loss; Meninges; Methods; Monitor; Nature; Neurodegenerative Disorders; Neurotransmitters; Noise; Outcome; Patients; Pattern; Performance; Perfusion; Persons; Physics; Population; Positron-Emission Tomography; Protocols documentation; Recovery; Resolution; Sample Size; Scanning; Scheme; Signal Transduction; Staging; Synapses; System; Testing; Thinness; Time; Tracer; Training; United States; Update; attenuation; base; deep learning; denoising; density; drug development; entorhinal cortex; human old age (65+); image reconstruction; imaging biomarker; imaging modality; improved; in vivo; kinetic model; neuroinflammation; pain relief; parametric imaging; pre-clinical; reconstruction; success; tau Proteins; tau aggregation; uptake; β-amyloid burden

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.

抽象的 阿尔茨海默病（AD）是一种进行性神经退行性疾病，其特征是记忆丧失、认知能力丧失 障碍和行为障碍。在美国，620 万 65 岁及以上的人患有 AD 2021 年各州。AD 的早期诊断具有特别重要的意义，因为治疗在 2021 年最有效 发生不可逆脑损伤之前的症状前阶段。 Tau 神经原纤维缠结（NFT）， 在症状出现前数十年的积累，可以表明症状出现前的阶段。根据布拉克 在分期中，tau NFT 从内嗅开始，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 治疗监测。随着最近 tau 示踪剂开发的突破，正电子发射 断层扫描（PET）可以检测体内 tau NFT 的积累。然而，由于信噪比（SNR） 由于 PET 的分辨率限制，在薄皮质区域准确恢复 tau 保留模式很困难。这是 对于 tau 信号较弱的早期阶段尤其如此。此外，最近的纵向研究表明 AD 组通过 PET 检测到的 tau 沉积物积累变化每年约为 3% 至 6%，而 AD 组则较小。 临床前AD组。这一年度小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，对 tau NFT 具有更高的亲和力，且无异味。 目标结合靠近早期 Braak 分期区域，这使得它对于早期 AD 诊断非常有希望。 然而，18F-MK-6240 的一个问题是脑膜中的脱靶结合。鉴于薄的性质 皮质带及其邻近脑膜，tau 积累的定量准确性显着 妥协了。因此，进一步提高 tau 成像的 PET 分辨率和 SNR 的需求尚未得到满足。 该资助申请提出了基于深度学习（DL）的图像重建方法，可以改善 tau 成像的分辨率和信噪比 (SNR)。该提案的四个具体目标是（1） 开发基于深度学习的静态 PET 图像重建； (2) 开发基于DL的动态图像重建 宠物; （3）开发能够快速生成高质量参数化图像的框架； (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 将是强大且临床有效的框架，可以生成具有改进的静态和参数图像 静态和简化的动态 tau PET 成像的分辨率和信噪比。