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.

摘要