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

基本信息

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
项目类别：
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&apos s Disease Alzheimer&apos s disease diagnosis Alzheimer&apos s disease patient Alzheimer&apos 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&apos s disease protein aggregation simulation success tau Proteins tau aggregation uptake

项目摘要

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.

使用无监督深度校正 PET 治疗阿尔茨海默病的部分体积效应学习抽象的阿尔茨海默病（AD）以记忆丧失和认知障碍为特征，影响美国约有 580 万人。对患者、家属和社会来说都是一种负担医疗保健系统。尸检研究表明，过度磷酸化的 Tau 蛋白聚集体与AD密切相关。随着选择性 Tau 示踪剂的发展，Tau 分布现在可以通过正电子发射断层扫描（PET）成像进行体内表征。根据布拉克分期，Tau 沉积物从临床前 AD 时颞叶的内嗅区开始，然后在后期扩散到其他皮层区域。通过捕获区域 Tau 分布模式因此，PET Tau 成像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。然而，薄层的精确量化由于 PET 成像中的部分体积效应 (PVE)，皮层摄取很困难。此外，对于第二个—— Tau 代示踪剂 18F-MK-6240，除了在新皮质和内侧观察到更高的摄取量之外在 AD 患者的颞脑区域，在以下区域也观察到非特异性摄取：脑膜。鉴于皮质带的薄特性及其靠近脑膜， 18F-MK-6240分布的定量准确性和检测精度受到显着影响，这反过来又妨碍了与临床前和临床前相关的早期 tau 积累的更精细定位。前驱期AD。该拨款申请提出了一种新颖的部分体积校正（PVC）方法 Tau 成像的无监督深度学习。这个新框架不需要高质量的培训标签和网络针对每个主题进行专门训练，训练目标函数根据正弦图数据的泊松分布假设制定。为进一步推动性能、迁移学习和内核学习都集成到这个 PVC 框架中。这该探索性提案的三个具体目标是（1）开发基于 PET PVC 框架无监督深度学习，(2) 使用模型研究验证所提出的 PVC 框架，以及 (3) 将所提出的 PVC 框架应用于 18F-MK-6240 成像数据集。我们期望具体目标的综合成果将是一种高效、实用和稳健的 PVC 方法，可以更好地解析Tau分布模式以用于AD的早期诊断。