Methods for Quantitative Neuroimaging of Tau Burden inPre-symptomatic AD

而淀粉样蛋白沉积倾向于广泛地发生在整个皮质区域，并具有相对均匀的模式 随着时间的推移，tau的分布在病理及其传播的早期阶段是高度集中的。 到其他地区则显示出更为复杂的时空演化。此外，相对较低的 与淀粉样蛋白相比，tau蛋白在脑中的丰度对结合亲和力和选择性提出了更高的要求 陶氏放射性示踪剂。尽管在过去几年中报道的第二代tau示踪剂似乎 在这方面，迄今为止研究的所有tau示踪剂都有偏离目标的报道。 与各种底物的结合可能会混淆成像数据的量化和解释。 这项应用的目标是开发创新的技术，提高tau PET的定量 成像，并增强其在研究和最终临床环境中的实用性。要解决tau的焦点性质 尤其是在疾病的前驱阶段，我们提出了一种新的基于核的重建方法 它使用结构磁共振图像作为先验信息，并结合运动校正以获得具有 前所未有的空间分辨率，允许改进tau在小型结构中的定位和量化，如 如鼻大脑皮层和蓝斑。为了减轻脱靶结合的影响，我们提出了一种动力学 分析策略以确定非特定的、tau特定的和偏离目标的成分。我们还提出了建模 评估脑血流灌注图和补充作为附加指标的tau负荷测量的策略 大脑功能的变化。最后，我们提出了深度学习策略，以获得对tau和tau传播的新见解 来预测个体受试者的这一进展。

Whereas amyloid deposition tends to occur broadly throughout cortical regions and with relatively uniform pattern of progression over time, the distribution of tau is highly focal at the early stages of pathology and its propagation to other regions demonstrates a more complex spatiotemporal evolution. In addition the relatively lower abundance in brain of tau compared to amyloid imparts greater requirements for binding affinity and selectivity of tau radiotracers. Although second generation tau tracers reported within the past few years appear to have more favorable properties in this regard, all tau tracers studied to date have been reported to exhibit off-target binding to various substrates that can confound the quantification and interpretation of imaging data. The goal of this application is to develop innovative technologies that improve the quantification of tau PET imaging and enhance its utility in both research and eventual clinical settings. To address the focal nature of tau deposition especially at prodromal stages of disease, we propose a novel kernel-based reconstruction method that uses structural MR images as prior information and incorporates motion correction to obtain images with unprecedented spatial resolution, allowing improved localization and quantification of tau in small structures such as the rhinal cortex and the locus coeruleus. To mitigate the effects of off-target binding, we propose a kinetic analysis strategies to identify non-specific, tau-specific, and off-target components. We also propose modeling tactics to estimate maps of cerebral perfusion and to complement measures of tau burden as an additional index of brain function. Lastly, we propose deep learning strategies to gain new insights into the spreading of tau and to predict this progression in individual subjects.