High-resolution cerebral microvascular imaging for characterizing vascular dysfunction in Alzheimer's disease mouse model

项目总结/摘要 阿尔茨海默病（AD）是痴呆症最常见的原因，影响着600万美国人的生活。 随着全球人口老龄化和缺乏有效的治疗方法，AD的全球流行正在逼近。在 目前，开发有效的AD疗法的关键挑战是复杂的病理生理过程， 是AD固有的。特别是，新出现的证据指出，血管功能障碍是一个早期和普遍存在的特征 的AD。然而，尽管血管对AD的贡献很重要，但目前在AD的治疗方面存在很大的差距。 了解血管-Abeta相互作用的确切机制。这一知识空白 从根本上限制了我们理解AD发病机制的关键机制， 开发有效的AD疗法。为了填补这一知识空白，进行体内机制研究至关重要 这是假设驱动的，提供了详细的了解脑血管疾病之间的关系， 损伤和AD病理生理学。为此，脑血管成像将发挥重要作用， 越来越重要的角色。然而，现有的脑血管成像技术不能提供 足够的成像空间分辨率和穿透深度，以提供血管生物标志物的测量 在含有AD微血管病变的脑深部区域。这一重大技术差距限制了我们的能力 回答关于AD神经血管病理学的重要问题。因此，本提案的目标是 开发一种基于超声的高分辨率脑微血管成像（HCMI）技术来填补这一空白。 重要技术差距，并最终提供一种可行的非侵入性成像工具来回答假设驱动 关于AD血管功能障碍和病理学的问题。如果成功开发，HCMI将实现整个- 脑、微米尺度和AD小鼠模型的动态微血管映射。在目标1中，我们将开发 HCMI的创新解决方案，可通过完整颅骨实现快速和强大的全脑微血管成像 纵向AD研究。我们将专注于开发可行的解决方案，实现可靠的纵向监测 整个大脑的微血管我们还将根据以下内容将HCMI从2D扩展到3D成像： 超高速3D超声波在目标2中，我们将使用基于FPGA和GPU的并行计算来加速HCMI 技术.我们将开发一种基于FPGA的超快波束形成器，用于连续高速超声数据 支持3D HCMI。我们还将开发基于GPU的并行计算方法，以最大限度地提高HCMI 后处理速度。在目标3中，我们将验证HCMI在AD小鼠模型上的体内性能。我们 将使用脑组织学和认知行为测试来验证HCMI在测量 与AD相关的微血管改变。基于最近的证据表明， 经颅微血管成像在人类与超声，有一个明确的途径，为未来的临床 翻译用于早期检测、疾病进展监测的拟议成像技术， AD临床疗效评价。

PROJECT SUMMARY/ABSTRACT Alzheimer’s disease (AD) is the most common cause of dementia and impacts the lives of 6 million Americans. With a worldwide aging population and absence of effective therapies, a global epidemic of AD is looming. At present, a key challenge for developing effective AD therapies is the complex pathophysiological processes inherent to AD. In particular, emerging evidence points to vascular dysfunction as an early and ubiquitous feature of AD. However, despite the importance of vascular contributions to AD, there currently exists a large gap in understanding the exact mechanisms underlying the vascular-Abeta interactions. This knowledge gap fundamentally limits our capabilities to understand key mechanisms underlying AD pathogenesis and ultimately develop effective AD therapies. To fill this knowledge gap, it is essential to conduct mechanistic in vivo studies that are hypothesis-driven, providing detailed understanding of the relationship between cerebrovascular impairments and AD pathophysiologies. To that end, cerebrovascular imaging will play an essential and increasingly important role. However, existing cerebrovascular imaging technologies fall short of providing adequate imaging spatial resolution and depth of penetration to provide measurements of vascular biomarkers in deep brain regions that contain AD microvascular pathologies. This significant technical gap limits our ability to answer important questions about neurovascular pathology in AD. Therefore, the goal of this proposal is to develop an ultrasound-based, high-resolution cerebral microvascular imaging (HCMI) technique to fill this important technical gap and ultimately provide a viable, noninvasive imaging tool to answer hypothesis-driven questions about AD vascular dysfunction and pathologies. If successfully developed, HCMI will achieve whole- brain, micron-scale, and dynamic microvascular mapping for AD mouse models. In Aim 1, we will develop innovative solutions for HCMI to enable fast and robust whole-brain microvascular imaging through intact skull for longitudinal AD studies. We will focus on developing viable solutions that allow reliable longitudinal monitoring of whole-brain microvasculature through intact skull. We will also extend HCMI from 2D to 3D imaging based on ultrafast 3D ultrasound. In Aim 2, we will accelerate HCMI with FPGA- and GPU-based parallel computing techniques. We will develop an FPGA-based ultrafast beamformer for continuous, high-speed ultrasound data acquisition to support 3D HCMI. We will also develop GPU-based parallel computing method to maximize HCMI post-processing speed. In Aim 3, we will validate the in vivo performance of HCMI on AD mouse models. We will use brain histology and cognitive behavioral testing to validate HCMI performance in measuring microvascular alterations associated with AD. Based on recent evidence indicating the feasibility of in vivo transcranial microvascular imaging in humans with ultrasound, there exists a clear pathway for future clinical translation of the proposed imaging technologies for early detection, disease progression monitoring, and therapy response evaluation of AD in clinic.