针对阿尔茨海默病早期诊断的11C-PiB PET图像感兴趣脑区研究

碳11-匹兹堡化合物B(11C-PiB) PET是针对β-淀粉样蛋白（Aβ）显像的最新技术，临床用于早期诊断阿尔茨海默病(AD)。但是，缺少明确的参考感兴趣脑区（ROIs）是该技术在AD早期诊断应用过程中亟待解决的瓶颈问题，导致医生诊断困难，无法客观追踪病情。针对传统PET图像ROIs定位方法,例如T检验与Z值统计模型等(1)无法考虑不同脑区Aβ沉淀的关联；(2)无法消除年龄等个体样本差异对Aβ沉淀影响的两点理论不足，本项目提出基于脑网络分析的11C-PiB PET图像ROIs定位方法，研究脑网络分析方法在考虑脑区间关联性和消除年龄对Aβ沉淀影响方面的优劣，获取ROIs并验证其在AD早期诊断中的临床意义。研究结果有望为医生提供新的11C-PiB PET图像ROIs模型，帮助医生更好地进行AD早期诊断。

Carbon 11-labeled Pittsburgh compound B PET (11C-PiB PET) for Amyloid-β (Aβ) peptide imaging is an advanced technology to diagnose Alzheimer’s disease (AD) in early stage. Lacking clear Regions of Interest (ROIs) is a key clinical requirement when implementing 11C-PiB PET technology, which results in difficulties to diagnose AD and track patients’ conditions. Existing ROIs identification methods for PET images, such as T test and Z statistic, have following theoretical limitations: (1) they cannot reflect the correlations of Aβ peptide among different brain areas; (2) they cannot overcome the influence of age factor for Aβ peptide. Hence, this project proposes brain network method to identify ROIs of 11C-PiB PET images; studies advantages and disadvantages of brain network method in considering the correlations among different brain areas and age correction; achieves ROIs and validates them. The outputs of this project are expected to provide new ROIs’ model, which is to aid radiologists to diagnose AD better in early stage.