Computational method to improve signal detection in NIRS instruments
改进 NIRS 仪器信号检测的计算方法
基本信息
- 批准号:8316125
- 负责人:
- 金额:$ 18.89万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2010
- 资助国家:美国
- 起止时间:2010-09-01 至 2015-08-31
- 项目状态:已结题
- 来源:
- 关键词:AffectAlgorithmsAreaAuditoryBasic ScienceBiologicalBiological ModelsBiological MonitoringBiological ProcessBiomedical ResearchBlinkingBloodBlood PressureBrainBreastCardiacClinical ResearchCognitiveComputational algorithmComputer softwareComputing MethodologiesDataDetectionDevelopmentDiagnosisDiseaseEffectivenessExcisionFrequenciesGoalsHealthImaging TechniquesInvestigationLaboratoriesLightMagnetic Resonance ImagingMeasuresMetalsMethodsModalityMonitorMorphologic artifactsMovementNear-Infrared SpectroscopyNeuronsNoiseOpticsOrganOrganismPathologyPerformancePhysiologicalPositron-Emission TomographyRegulationResearch PersonnelResearch ProposalsResolutionRespirationScalp structureSensitivity and SpecificitySignal TransductionSkinStagingSurfaceSystemTechniquesTechnologyTestingTissuesVariantVasomotorVisualabsorptionbasebody systemchromophoreclinical practicecomputerized data processingcomputerized toolscostcraniumdata acquisitionhemodynamicsimprovedin vivoindependent component analysisinstrumentinstrumentationinterestmillisecondneurophysiologynew technologynovelobject recognitionoptical imagingportabilitypublic health relevanceresearch studyresponserestorationtoolvascular bed
项目摘要
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.
描述(由申请人提供):近红外光谱(NIRS)是一项有前途且发展迅速的技术,具有便携性、低成本和多功能等多种独特特征。它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浅表组织层以及其他伪影和系统生理信号(称为“全局干扰”)的贡献。这些不良影响在测量光信号强度的连续波 NIRS 仪器中尤其突出。我们的提案是对 RFA-RR-09-001 的回应,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 NIRS 仪器的灵敏度、选择性和信噪比。它包括 1) 将信号分解为统计上独立的分量,2) 识别人为分量,以及 3) 通过信号恢复将其去除。我们的初步数据表明,通过基于 ICA 的信号处理,可以显着提高 NIRS 信号中功能相关变化的信噪比。因此,可以以毫秒范围内的时间分辨率可靠地记录光信号中与功能相关的瞬态变化。该项目的目标是:1)通过基于 ICA 的算法开发改进的仪器和数据采集方法,以可靠地检测感兴趣区域组织深层的光学光谱信号; 2) 证明 ICA 方法在健康受试者神经生理学实验中的有效性,在视觉和听觉模态快速物体识别的认知任务中测量血流动力学和快速光学信号; 3) 将 ICA 方法实施为软件工具箱,用作 NIRS 仪器的组成部分,使研究人员能够在实验过程中优化数据采集设置。我们将开发、测试、验证和表征所提出的计算算法在提高非侵入式 NIRS 技术功能方面的有效性。该研究将确定该方法在改善健康和疾病中生物组织的无创评估和监测方面的潜在效用。
公共健康相关性(由申请人提供):近红外光谱 (NIRS) 等非侵入性光学成像技术比用于监测生物组织的 MRI 或 PET 等现有技术具有许多潜在优势。 NIRS 技术对受试者体内金属物体的存在不敏感(这是 MRI 研究的阻碍因素),对受试者运动不太敏感,而且相对便宜。近红外光谱应用的限制因素之一是组织表层的影响,它掩盖了深层的贡献,并可能产生不需要的噪声成分(伪影)。在拟议的研究中,我们将开发一种计算方法,以基于新颖的信号处理技术(独立成分分析或 ICA)来提高 NIRS 仪器的灵敏度和选择性。去噪和优化算法的成功应用将鼓励低成本 NIRS 技术在基础研究和临床实践的各种可能应用中的进一步应用。我们的项目将为进一步开发计算工具奠定基础,改进 NIRS 技术并提高其在各种功能条件下监测生物组织的实用性。随着灵敏度和选择性的提高,NIRS 技术将在基础研究以及床边多种病理类型的临床研究中得到广泛应用。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
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{{ truncateString('ANDREI V MEDVEDEV', 18)}}的其他基金
Computational method to improve signal detection in NIRS instruments
改进 NIRS 仪器信号检测的计算方法
- 批准号:
8133477 - 财政年份:2010
- 资助金额:
$ 18.89万 - 项目类别:
Computational method to improve signal detection in NIRS instruments
改进 NIRS 仪器信号检测的计算方法
- 批准号:
7944918 - 财政年份:2010
- 资助金额:
$ 18.89万 - 项目类别:
Non-invasive Fast Optical Imaging of Visual and Motor Processing
视觉和运动处理的非侵入式快速光学成像
- 批准号:
7285551 - 财政年份:2006
- 资助金额:
$ 18.89万 - 项目类别:
Non-invasive Fast Optical Imaging of Visual and Motor Processing
视觉和运动处理的非侵入式快速光学成像
- 批准号:
7477072 - 财政年份:2006
- 资助金额:
$ 18.89万 - 项目类别:
Non-invasive Fast Optical Imaging of Visual and Motor Processing
视觉和运动处理的非侵入式快速光学成像
- 批准号:
7139857 - 财政年份:2006
- 资助金额:
$ 18.89万 - 项目类别:
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