Correction of Partial Volume Effects in PET for Alzheimer's Disease Using Unsupervised Deep Learning

使用无监督深度学习校正阿尔茨海默病 PET 中的部分体积效应

• 批准号: 9974892
• 负责人: Kuang Gong
• 金额: $ 45.3万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-15 至 2023-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9974892
• 关键词: Affect; Alzheimer' s Disease; Alzheimer' s disease diagnosis; Alzheimer' s disease patient; Alzheimer' s disease therapy; Applications Grants; Area; Autopsy; Behavior Disorders; Binding; Binding Sites; Brain; Brain Injuries; Brain region; Clinical Trials; Computer Simulation; Computer software; Data; Data Set; Dementia; Deposition; Detection; Development; Disease Progression; Early Diagnosis; Elderly; Family; Future; Generations; Goals; Healthcare Systems; Hippocampus (Brain); Image; Impaired cognition; Label; Longitudinal Studies; Magnetic Resonance; Magnetic Resonance Imaging; Medial; Memory Loss; Meninges; Methods; Monitor; Nature; Neurodegenerative Disorders; Neurotransmitters; Outcome; Patients; Pattern; Performance; Poisson Distribution; Positron-Emission Tomography; Protocols documentation; Psychological Transfer; Recovery; Resolution; Sample Size; Scanning; Scheme; Signal Transduction; Staging; Structure of choroid plexus; Synapses; System; Temporal Lobe; Testing; Thinness; Tissues; Tracer; Training; United States; Work; base; cognitive testing; deep learning; density; disease diagnosis; drug development; effective therapy; entorhinal cortex; hyperphosphorylated tau; improved; in vivo; innovation; kernel methods; neocortical; novel; pain patient; pain relief; pre-clinical; prodromal Alzheimer' s disease; protein aggregation; simulation; success; tau Proteins; tau aggregation; uptake

Correction of Partial Volume Effects in PET for Alzheimer's Disease Using Unsupervised Deep Learning Abstract Alzheimer's disease (AD), characterized by memory loss and cognitive impairments, affects approximately 5.8 million people in the United States. It is a burden for patients, families and the healthcare system. Post-mortem studies reveal that hyperphosphorylated Tau protein aggregates are closely related with AD. With the developments of selective Tau tracers, Tau distributions can now be characterized in vivo through Positron emission tomography (PET) imaging. According to the Braak staging, Tau deposits start from the transentorhinal region of the temporal lobe at preclinical AD, then spreading to other cortex regions at late stages. Regional Tau distribution pattern captured through PET Tau imaging can thus provide important staging information, which is vital for early disease diagnosis, progression tracking and treatment monitoring. However, accurate quantification of thin cortex uptake is difficult due to partial volume effects (PVEs) in PET imaging. Besides, for the second- generation Tau tracer, 18F-MK-6240, apart from higher uptakes observed in neocortical and medial temporal brain regions for AD patients, nonspecific uptakes are also observed in areas such as the meninges. Given the thin nature of the cortical ribbon and its close proximity to the meninges, quantitative accuracy and detection precision of 18F-MK-6240 distributions are significantly impacted, which in turn precludes finer localization of early tau accumulation associated with preclinical and prodromal AD. This grant application proposes a novel partial volume correction (PVC) method through unsupervised deep learning for Tau imaging. This new framework does not need high-quality training labels and the network is specifically trained for each subject, with the training objective function formulated based on the Poisson distribution assumption of the sinogram data. To further boost the performance, the transfer learning and the kernel learning are integrated into this PVC framework. The three specific aims of this exploratory proposal are (1) to develop a PET PVC framework based on unsupervised deep learning, (2) to validate the proposed PVC framework using phantom studies and (3) to apply the proposed PVC framework to 18F-MK-6240 imaging datasets. We expect that the integrated outcome of the specific aims will be an efficient, practical and robust PVC method that can better resolve the Tau distribution patterns for the early diagnosis of AD.

使用无监督的深处，对阿尔茨海默氏病PET的部分体积效应纠正 学习 抽象的 阿尔茨海默氏病（AD）以记忆力丧失和认知障碍为特征 美国约有580万人。这是患者，家庭和 医疗保健系统。验尸研究表明，高磷酸化的tau蛋白聚集体是 与AD密切相关。随着选择性tau示踪剂的发展，tau分布现在可以是 通过正电子发射断层扫描（PET）成像在体内表征。根据武器 分阶段，tau沉积物从临时AD处的颞叶的横向区域开始，然后 在后期散布到其他皮质区域。通过捕获的区域tau分配模式 PET TAU成像因此可以提供重要的分期信息，这对于早期疾病至关重要 诊断，进展跟踪和治疗监测。但是，精确定量薄 由于PET成像中的部分体积效应（PVE），因此很难摄取皮层的摄取。此外，第二次 Tau Generation Tracer，18F-MK-6240，除了在新皮层和内侧观察到的较高摄入量 AD患者的颞大脑区域，在等区域也观察到非特异性摄取 脑膜。考虑到皮质色带的薄质及其与脑膜的近端 18F-MK-6240分布的定量准确性和检测精度受到显着影响， 这反过来排除了与临床前和临床前和 前序广告。该赠款申请通过 无监督的深度学习tau成像。这个新框架不需要高质量的培训 标签和网络是针对每个主题的专门培训的，并具有培训目标功能 基于辛克图数据的泊松分布假设制定。为了进一步提高 性能，转移学习和内核学习已集成到该PVC框架中。这 该探索性建议的三个具体目标是（1）基于PET PVC框架 无监督的深度学习，（2）使用幻影研究和 （3）将建议的PVC框架应用于18F-MK-6240成像数据集。我们期望 特定目标的综合结果将是一种有效，实用和强大的PVC方法，可以 更好地解决AD早期诊断的TAU分布模式。