Quantitative Diffuse Correlation Spectroscopy for Assessing Human Brain Function

项目总结/摘要 急性脑损伤可能导致继发性脑损伤，从而影响预后。脑血减少 血流可引起局部缺血，而过多的血流可引起出血。因此，需要 神经重症监护病房的无创床旁连续脑血流监测方法 （NICU）。用于连续监测脑血流的现有技术具有严重的局限性。 功能性近红外光谱已被用于这种临床需要，但它遭受不 定量的并且由于来自表面头皮组织的信号而易于出错。此外，它只测量有限的 氧饱和度的信息含量。额外的血流对比可以提供有用的生物标志物。弥漫性 相关光谱（DCS）技术是一种新兴的用于床边监测的扩散光学技术， 人体的血液流动目前，DCS在连续波（CW）模式下运行，其具有以下局限性： 由于依赖于先验信息而导致表面信号灵敏度和血流的不准确量化 光学参数。最近的时域（TD）方法具有低信噪比、成本高、高性能等优点。 限于临床翻译。我们的目标是通过提出一种新技术来解决这些限制， 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， 快速的数据采集，因此，它非常适合于快速功能神经成像。它也可以将表面的 和大脑信号通过区分早期和晚期光子通过时间门控。此外，波长较长， 红外线允许增强的深度穿透。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环境。所提出的系统和方法将 完全取代目前最先进的（CW-DCS），是上级TD方法，因为它可以提供 大脑中更高的信噪比（SNR），它的简单性和仪器成本显着降低， 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 原型，表征信号，然后我们将在体模模型和定制开发上测试系统 分析和蒙特卡罗和深度学习模型，并确定量化精度， 静态和动态参数（Aim-1）。我们将在脉冲宽度、SNR方面对系统进行优化， 提高了静态和动态参数的量化精度（Aim-2）。然后，我们将测试该系统， 健康受试者和创伤性脑损伤患者（Aim-3）。这种创新的DCS系统和方法将 导致具有增强的大脑灵敏度的定量血流参数，并将消除 CW和TD方法，从而将为NICU环境中的快速临床翻译和 一般神经成像应用。

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.