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

使用随机对象模型对小鼠大脑进行体内β-淀粉样蛋白成像

• 批准号: 9002044
• 负责人: Sepideh Shokouhi
• 金额: $ 11.95万
• 依托单位: VANDERBILT UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-02-01 至 2016-04-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9002044
• 关键词: Age; Algorithms; Alzheimer' s Disease; Amyloid beta-Protein; Animal Model; Animals; Brain; Brain region; Characteristics; Classification; Computational algorithm; Computing Methodologies; Data; Detection; Diagnosis; Disease; Evaluation; Goals; Human; Image; In Vitro; Measures; Methods; Modeling; Monitor; Monte Carlo Method; Mus; Nuclear; Pathology; Pattern Recognition; Phase; Positron-Emission Tomography; Procedures; Process; Research; Resolution; Sampling; Scanning; Senile Plaques; Signal Transduction; Structure; Techniques; Therapeutic; Tracer; Training; Transgenic Mice; Transgenic Organisms; Translating; Variant; animal imaging; drug testing; experience; image reconstruction; imaging modality; imaging system; improved; in vivo; in vivo imaging; instrumentation; molecular imaging; novel; pre-clinical; radiotracer; reconstruction; single photon emission computed tomography; skills; tool

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.

拟议的研究的总体目标是开发新颖的体内成像方法（3个斑块） 使用正电子发射断层扫描（PET）在转基因小鼠中的检测和分类。减少 淀粉样β斑块是治疗阿尔茨海默氏病的主要治疗目标之一 （广告）。使用PET的AP成像现已进入修订的AD诊断标准的领域。体内 小鼠大脑中AP的成像将促进研究AD病理学的研究和可能的药物测试 停止或减速疾病进展。但是，斑块的异质微观结构使得 由于分辨率不足而导致的斑块负担挑战性的一种体内方法。 同样，当前的图像重建技术并不能很好地适应异质的重建 微观结构。候选人正在提出一种利用随机对象模型的方法 AP改善体内成像。该模型的参数是从包含的分布中取样的 有关不同大脑区域斑块的大小和数量的信息以及它们之间的变化 动物和随着时间的变化。这些参数是使用高级的体外数据获得的 立体分析方法。随机对象模型将纳入优化 方法，例如模拟退火。此过程的目的是搜索随机实现的 来自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 通过最小化目标函数来系统。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。这也是她过去经历的逻辑扩展 宠物/光谱仪器和图像重建。 ROO阶段大纲研究的具体目的 通过动物扫描探索该技术的可行性。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IRI体内成像系统的实际局限性 保持着关注他们在广告研究中的实施。