Computational method to improve signal detection in NIRS instruments

描述(申请人提供)：近红外光谱(NIRS)是一项前景看好、发展迅速的技术，具有便携性、低成本和多功能等特点。这是唯一对组织血流动力学(慢信号)和神经元活动(快信号)敏感的光谱技术。限制其应用的问题之一是记录信号中的浅层组织层以及其他伪影和系统生理信号的贡献，这些伪影和系统生理信号被称为“全局干扰”。这些不良影响在测量光信号强度的连续波近红外仪器中尤为突出。我们的建议是对RFA-RR-09-001的响应，旨在通过使用基于独立分量分析(ICA)的先进信号处理技术优化数据采集，提高近红外仪器的灵敏度、选择性和信噪比。它包括1)将信号分解成统计独立的分量，2)识别伪迹分量，3)通过信号恢复去除伪迹分量。我们的初步数据表明，通过基于ICA的信号处理，可以显著提高近红外信号中功能相关变化的信噪比。结果，可以以毫秒范围内的时间分辨率可靠地记录光学信号中的功能相关的瞬时变化。拟议项目的目的是：1)通过基于ICA的算法开发改进的仪器和数据采集方法，以可靠地检测来自感兴趣区域更深层次组织的光学光谱信号；2)在神经生理学实验中展示ICA方法的有效性，该实验使用健康受试者在视觉和听觉模式下快速识别物体的认知任务中测量血流动力学和快速光学信号；以及3)将ICA方法作为软件工具箱实施，作为近红外光谱仪器不可分割的一部分，使研究人员能够在实验期间优化数据采集设置。我们将根据其在增加非侵入性近红外光谱技术功能方面的有效性来开发、测试、验证和表征所提出的计算算法。这项研究将确定该方法在改进对健康和疾病中的生物组织的非侵入性评估和监测方面的潜在效用。 公共卫生相关性(由申请人提供)：非侵入性光学成像技术，如近红外光谱(NIRS)，为监测生物组织提供了许多潜在的优势，而不是现有的技术，如核磁共振或正电子发射计算机断层扫描。近红外光谱技术对受试者体内金属物体的存在不敏感(这是磁共振研究的一个禁止因素)，对受试者的移动不那么敏感，而且相对便宜。近红外光谱应用中的限制因素之一是表层组织的影响，它掩盖了深层的贡献，并可能产生不希望看到的噪声成分(伪影)。在拟议的研究中，我们将开发一种计算方法来提高近红外仪器的灵敏度和选择性，该方法基于一种新的信号处理技术(独立分量分析)。去噪和优化算法的成功应用将鼓励低成本近红外光谱技术在基础研究和临床实践中的各种可能应用中进一步应用。我们的项目将为进一步开发计算工具、改进近红外光谱技术并增加其在各种功能条件下监测生物组织的有效性奠定基础。随着灵敏度和选择性的提高，近红外光谱技术将在床边环境下的基础研究和临床研究中找到许多应用。

DESCRIPTION (provided by applicant): Near-infrared spectroscopy (NIRS) is a promising and rapidly developing technology with several unique features such as portability, low cost and multi-functionality. It is the only spectroscopic technique which is sensitive to tissue hemodynamics (slow signal) and neuronal activity (fast signal). One of the problems limiting its application is the contribution of superficial tissue layers in the recorded signal as well as other artifacts and systemic physiological signals termed "global interference". Those undesirable effects are especially prominent in continuous-wave NIRS instruments, which measure the intensity of the optical signal. Our proposal is a response to RFA-RR-09-001 and aims to improve the sensitivity, selectivity and signal-to-noise ratio of the NIRS instruments through optimization of data acquisition using the advanced signal processing technique based on Independent Component Analysis (ICA). It includes 1) signal decomposition into the statistically independent components, 2) identification of artifactual components and 3) their removal through signal restoration. Our preliminary data show that the signal-to-noise ratio of the functionally relevant changes in the NIRS signal can be significantly improved through the ICA-based signal processing. As a result, functionally relevant transient changes in the optical signal can be reliably recorded with a temporal resolution in the millisecond range. The aims of the proposed project are: 1) to develop improved instrumentation and data acquisition methods through ICA-based algorithms to reliably detect optical spectroscopic signals from the deeper layers of tissue in the area of interest; 2) to demonstrate the effectiveness of the ICA method in neurophysiological experiments with healthy subjects measuring the hemodynamic and fast optical signals during cognitive tasks of rapid object recognition in visual and auditory modality; and 3) to implement the ICA method as a software toolbox to be used as an integral part of the NIRS instruments allowing the investigator to optimize the data acquisition setup during the experiment. We will develop, test, validate and characterize the proposed computational algorithm in terms of its effectiveness in increasing the functional capabilities of non-invasive NIRS technology. The study will establish the potential utility of the method for improved noninvasive assessment and monitoring of biological tissue in health and disease. PUBLIC HEALTH RELEVANCE (provided by the applicant): Noninvasive optical imaging techniques, such as near-infrared spectroscopy (NIRS), offer a number of potential advantages over existing techniques such as MRI or PET for monitoring biological tissue. NIRS technology is insensitive to the presence of metal objects in the subject's body (a prohibiting factor for MRI studies), less sensitive to subject movement and relatively inexpensive. One of the limiting factors in the application of NIRS is the effect of superficial layers of tissue which obscure the contribution of the deeper layers and may create undesirable noisy components (artifacts). In the proposed studies, we will develop a computational method to improve the sensitivity and selectivity of the NIRS instruments based on a novel signal processing technique (Independent Component Analysis or ICA). The demonstrated successful application of de-noising and optimization algorithms would encourage further application of low-cost NIRS technology in a wide variety of possible applications in basic research and clinical practice. Our project will set a stage for further development of computational tools improving the NIRS technology and increasing its usefulness for monitoring biological tissue during various functional conditions. With increased sensitivity and selectivity, the NIRS technology will find numerous applications in the basic research as well as clinical studies of many types of pathology in a bedside setting.