Methods for Quantitative Neuroimaging of Tau Burden inPre-symptomatic AD

而淀粉样蛋白沉积倾向于广泛发生在整个皮质区域，并且具有相对均匀的模式 随着时间的推移，tau的分布在病理学的早期阶段是高度集中的， 向其他地区的扩散则呈现出更为复杂的时空演变。此外，相对较低的 与淀粉样蛋白相比，tau在脑中的丰度赋予了对结合亲和力和选择性的更高要求 tau放射性示踪剂。尽管在过去几年中报道的第二代tau示踪剂似乎具有 在这方面，迄今为止研究的所有tau示踪剂都被报道表现出脱靶 与各种基质结合，可能会混淆成像数据的量化和解释。 该应用的目标是开发创新技术，改善tau PET的定量 成像和增强其在研究和最终临床环境中的实用性。为了解决tau的焦点性质， 特别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 其使用结构MR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，允许在小结构中改进tau的定位和定量， 和蓝斑一样。为了减轻脱靶结合的影响，我们提出了一种动力学 分析策略以鉴定非特异性、tau特异性和脱靶组分。我们还建议建模 评估脑灌注图和补充tau负荷测量作为额外指标的策略 大脑功能。最后，我们提出了深度学习策略，以获得对tau传播的新见解， 来预测个体受试者的这种进展。

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.