Quantitative Diffuse Correlation Spectroscopy for Assessing Human Brain Function

项目摘要/摘要 急性脑损伤可能导致继发性脑损伤，使预后恶化。脑血量减少 血流会导致缺血，而过度的血液流动会导致出血。因此，有必要 神经重症监护病房的无创、床边、持续脑血流监测方法 (NICU)。现有的连续监测脑血流的技术有严重的局限性。 功能性近红外光谱学已经被用于这一临床需求，但它还没有被采用。 由于头皮浅层组织发出的信号，容易出现定量的错误。此外，它只测量有限的 血氧饱和度的信息量。额外的血流对比可以提供一个有用的生物标志物。漫射 相关光谱(DCS)技术是一种新兴的床边监测漫反射光学技术 人体内的血液流动。目前，分散控制系统以连续波(CW)模式运行，具有以下限制 由于对先验信息的依赖，对血流量的表面信号敏感性和不准确的量化 光学参数。最近的时域(TD)方法具有低信噪比、成本高、 仅限于临床翻译。目标是通过提出一种新的技术和 一种可以在单个仪器中同时量化绝对静态和动态参数的方法 数据采集速度快，非常适合于快速功能神经成像。它还可以将表面的 和大脑信号，通过时间门控区分早期和晚期光子。此外，更长的波长在 红外线可以增强深度穿透能力。它可以量化血流和光学参数在近- 实时使用深度学习，非常适合NICU设置。所提议的系统和方法将 完全取代当前最先进的(CW-DCS)，是优越的TD方法，因为它可以提供 大脑中更高的信噪比(SNR)，其简单性和显著降低的仪器成本， 这将导致快速的临床翻译。为了实现我们的目标，我们将构建和优化仪器 样机，对信号进行表征，然后我们将在人体模型和定制开发的模型上测试系统 分析、蒙特卡罗和深度学习模型，并从以下方面确定量化精度 静态和动态参数(AIM-1)。我们将在脉冲宽度、信噪比等方面对系统进行优化 改进了静态和动态参数的量化精度(AIM-2)。然后，我们将在 健康受试者和脑外伤患者(AIM-3)。这一创新的分布式控制系统和方法将 产生定量血流参数，增强大脑敏感度，并将消除 CW和TD方法，从而将为NICU环境下的快速临床转换铺平道路 一般的神经成像应用。

PROJECT SUMMARY/ABSTRACT Acute brain injuries can lead to secondary brain damage that worsens the outcome. Reduced cerebral blood flow can induce ischemia, while excess blood flow can cause hemorrhage. Thus, there is a need for noninvasive, bedside, continuous cerebral blood flow monitoring approaches at neurointensive care units (NICUs). Existing technologies for continuous monitoring of cerebral blood flow have critical limitations. Functional near-infrared spectroscopy has been employed for this clinical need, but it suffers from being not quantitative and prone to errors due to signals from superficial scalp tissue. Moreover, it measures only limited information content of oxygen saturation. Additional blood flow contrast can provide a useful biomarker. Diffuse correlation spectroscopy (DCS) technique is an emerging diffuse optical technique for bedside monitoring of blood flow in humans. Currently, DCS operates in continuous-wave (CW) mode, which has limitations such as superficial signal sensitivity and inaccurate quantification of blood flow due to dependency to priori information of optical parameters. More recent time domain (TD) approach has low signal-to-noise ratio, costly, highly limited for clinical translation. The goal is to address these limitations by proposing a novel technology and method that can quantify both absolute static and dynamic parameters concurrently in a single instrument with fast data acquisition, thus, it is highly suitable for fast functional neuroimaging. It can also separate superficial and brain signals by discriminating early and late photons via time-gating. Additionally, longer wavelength at the infrared allows for enhanced depth penetration. It can quantify blood flow and optical parameters in near- real-time using deep learning, which is highly suitable for NICU settings. The proposed system and method will completely replace the current state-of-the-art (CW-DCS) and is superior TD approach, because it can provide higher signal-to-noise ratio (SNR) in the brain, its simplicity and significantly lower cost in instrumentation, which will lead to fast clinical translation. To achieve our goal, we will construct and optimize the instrument prototype, characterize the signal, and then we will test the system on phantom models and custom-developed analytical and Monte Carlo and deep learning models and determine the quantification accuracy with respect to static and dynamic parameters (Aim-1). We will optimize the system with respect to pulse-width, SNR for improved quantification accuracy of static and dynamic parameters (Aim-2). Then, we will test the system in healthy subjects and traumatic brain injury patients (Aim-3). This innovative DCS system and method will result in quantitative blood flow parameter with enhanced brain sensitivity and will eliminate the roadblocks in both CW and TD approaches, thereby will pave the way for fast clinical translation at NICU settings and for general neuroimaging applications.