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

无监督深度校正阿尔茨海默病PET的部分体积效应 学习 摘要 阿尔茨海默病(AD)以记忆力丧失和认知障碍为特征，影响 美国大约有580万人。这对患者、家属和 医疗保健系统。死后研究表明，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的分布现在可以 通过正电子发射断层扫描(PET)成像进行体内表征。根据Braak的说法 临床前AD时，Tau沉积开始于颞叶的经鼻区域，然后 在后期扩散到其他皮质区域。通过捕获的区域Tau分布模式 因此，PET Tau成像可以提供重要的分期信息，这对早期疾病至关重要 诊断、进展跟踪和治疗监测。然而，准确量化薄层 在PET成像中，由于部分体积效应(PVE)，皮质摄取是困难的。再说了，第二点- 新一代Tau示踪剂18F-MK-6240，除了在新皮质和内侧观察到更高的摄取 对于AD患者的颞脑区域，也观察到非特异性摄取在诸如 脑膜。考虑到皮质带的薄性质及其与脑膜的密切联系， 18F-MK-6240分布的定量准确度和检测精度受到显著影响， 这反过来又排除了与临床前和临床前相关的早期tau蓄积的更精细定位 前驱AD。这项拨款申请提出了一种新的部分体积校正(PVC)方法，通过 用于Tau成像的无监督深度学习。这一新框架不需要高质量的培训 标签和网络是针对每个受试者专门训练的，具有训练目标函数 基于正弦图数据的泊松分布假设而制定的。为了进一步推动 该框架将性能学习、迁移学习和核学习有机地结合在一起。这个 这一探索性建议的三个具体目标是：(1)开发基于以下内容的PET PVC框架 无监督深度学习，(2)使用模型研究和 (3)将所提出的PVC框架应用于18F-MK-6240成像数据集。我们预计， 具体目标的综合结果将是一种高效、实用和稳健的PVC方法，它可以 较好地解析Tau分布规律，有助于AD的早期诊断。

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.