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

拟议研究的总体目标是-开发A(3)斑块的新的活体成像方法 利用正电子发射断层扫描(PET)对转基因小鼠进行检测和分类。减少了 淀粉样β斑块是治疗阿尔茨海默病的主要治疗目标之一 (Ad)。经修订的阿尔茨海默病诊断标准，现已进入正电子发射计算机断层扫描。活体内 小鼠脑内AP的成像将有助于AD病理的研究和药物的测试 阻止或减缓疾病的进展。然而，斑块的不均一微结构使 一种体内检测和量化斑块负荷的方法，由于分辨率不足而具有挑战性。 此外，现有的图像重建技术也不能很好地适应异质图像的重建 微结构。候选人提出了一种方法，该方法利用随机对象模型 AP改善活体成像。该模型的参数是从包含以下内容的分布中抽样的 不同脑区斑块的大小和数量以及它们在不同脑区之间的差异 动物和加班加点的变化。这些参数是从体外数据中使用先进的 体视学分析方法。随机目标模型将被合并到优化中 方法，如模拟退火法。这个过程的目标是寻找随机实现的 在成像的估计数据和测量数据之间提供最佳拟合的对象模型 系统通过最小化目标函数来实现。候选人在K99阶段的培训提供了所需的 在这个跨学科领域站稳脚跟的技能。这也是她过去经历的合乎逻辑的延伸 PET/SPECT器械和图像重建。Roo阶段大纲研究的具体目标是 通过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，以克服虹膜活体成像系统的实用限制，同时 继续把重点放在AD研究中的实施上。

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.