Quantitative Diffuse Correlation Spectroscopy for Assessing Human Brain Function

用于评估人脑功能的定量漫相关光谱

• 批准号: 10754343
• 负责人: Ulas Sunar
• 金额: $ 38.78万
• 依托单位: STATE UNIVERSITY NEW YORK STONY BROOK
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-05 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10754343
• 关键词: Quantitative; Diffuse; Correlation; Spectroscopy; Assessing

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.

项目概要/摘要 急性脑损伤可导致继发性脑损伤，从而使结果恶化。脑血减少 血流过多会引起缺血，而血流过多会导致出血。因此，需要 神经重症监护病房的无创床边连续脑血流监测方法 （新生儿重症监护病房）。现有的连续监测脑血流的技术具有严重的局限性。 功能性近红外光谱已被用于满足这一临床需求，但它的缺点是不适用 定量的，并且由于来自浅表头皮组织的信号而容易出错。此外，它只能测量有限的 氧饱和度的信息内容。额外的血流对比可以提供有用的生物标志物。扩散 相关光谱（DCS）技术是一种新兴的漫射光学技术，用于床边监测 人体的血液流动。目前，DCS以连续波（CW）模式运行，该模式存在以下局限性： 由于依赖先验信息，导致信号敏感性肤浅且血流定量不准确 的光学参数。最近的时域 (TD) 方法信噪比低、成本高、 仅限于临床翻译。目标是通过提出一种新技术来解决这些限制 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，因此非常适合快速功能神经成像。它还可以分离表面 以及通过时间选通区分早期和晚期光子的大脑信号。此外，更长的波长 红外线可以增强深度穿透力。它可以量化近处的血流和光学参数 实时使用深度学习，非常适合 NICU 设置。所提出的系统和方法将 完全取代当前最先进的（CW-DCS）并且是优越的TD方法，因为它可以提供 大脑中的信噪比（SNR）更高，其简单性和仪器成本显着降低， 这将导致快速的临床转化。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，然后我们将在模型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 方面优化系统 提高静态和动态参数的量化精度（Aim-2）。然后，我们将测试系统 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 产生定量血流参数，增强大脑敏感性，并消除障碍 CW 和 TD 方法，从而将为 NICU 环境中的快速临床转化和 一般神经影像应用。