In vivo Amyloid-Beta Imaging in Mouse Brain Using Stochastic Object Models

本研究的总体目标是开发新的A β斑块体内成像方法 使用正电子发射断层扫描（PET）在转基因小鼠中进行检测和分类。减少 β淀粉样蛋白斑块是治疗阿尔茨海默病的主要治疗目标之一 （AD）。PET的AP成像现在已经进入了AD诊断的修订标准领域。体内 小鼠脑中Ap的成像将有助于AD病理学的研究和药物的测试， 阻止或减缓疾病进展。然而，斑块的异质微观结构使得 由于分辨率不足，检测和量化斑块负荷的体内方法具有挑战性。 此外，当前的图像重建技术不能很好地适应于异构图像的重建。 微观结构候选人提出了一种方法，利用随机对象模型， AP以改善体内成像。该模型的参数从包含以下项的分布中采样： 关于不同脑区斑块的大小和数量以及它们在不同脑区之间的变化的信息。 动物和变化超时。这些参数是从体外数据获得的，使用先进的 体视学分析方法随机对象模型将被纳入一个优化 方法，如模拟退火。这个过程的目的是寻找一个随机实现的 提供来自成像的估计数据和测量数据之间的最佳拟合的对象模型 系统通过最小化目标函数。候选人在K99阶段的培训提供了所需的 在这个跨学科领域建立自己的技能。这也是她过去的经验在逻辑上的延伸， PET/SPECT仪器和图像重建。地区办事处阶段大纲研究的具体目标， 用动物扫描来探索这项技术的可行性。候选人的长期目标是开发信号 检测和模式识别工具来克服虹膜活体成像系统的实际限制， 保持她对他们在AD研究中的实施的关注。

The overall goal of the proposed research is-to develop novel in vivo imaging methods for A(3 plaque detection and classification in transgenic mice using positron emission tomography (PET). The reduction of amyloid beta plaques is among the main therapeutic objectives for the treatment of Alzheimer's disease (AD). Ap imaging with PET has now entered the realm of the revised criteria for diagnosis of AD. In vivo imaging of Ap in mouse brain would facilitate the study of the AD pathology and testing of drugs that could stop or decelerate the disease progress. However, the heterogeneous microstructure of the plaques makes an in vivo approach to detecting and quantifying the plaque burden challenging due to insufficient resolution. Also, current image reconstruction techniques are not well adapted to the reconstruction of heterogeneous microstructures. The candidate is proposing a method that takes advantage of a stochastic object model of AP to improve in vivo imaging. The parameters of this model are sampled from distributions that contain information about the size and number of plaques in different brain regions as well as their variations among animals and changes overtime. These parameters are obtained from in vitro data using advanced stereological analysis methods. The stochastic object model will be incorporated into an optimization method, such as simulated annealing. The objective of this process is to search for a random realization of the object model that provides the best fit between the estimated and the measured data from the imaging system by minimizing a target function. The candidate's training in the K99 phase has provided the required skills to establish herself in this interdisciplinary field. It is also a logical extension of her past experience in PET/SPECT instrumentation and image reconstruction. The specific aims of the ROO phase outline studies to explore the feasibility of this technique with animal scans. The candidate's long-term goal is to develop signal detection and pattern recognition tools to overcome the practical limitations of iri vivo imaging systems while maintaining her focus on their implementation in AD research.