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近红外光谱学中源探测器分离在健康和临床应用中的研究。

Investigation of the source-detector separation in near infrared spectroscopy for healthy and clinical applications.

机构信息

Drexel University, School of Biomedical Engineering, Science and Health Systems, Philadelphia, Pennsylvania.

Department of Family and Community Health, University of Pennsylvania, Philadelphia, Pennsylvania.

出版信息

J Biophotonics. 2019 Nov;12(11):e201900175. doi: 10.1002/jbio.201900175. Epub 2019 Jul 23.

Abstract

Understanding near infrared light propagation in tissue is vital for designing next generation optical brain imaging devices. Monte Carlo (MC) simulations provide a controlled mechanism to characterize and evaluate contributions of diverse near infrared spectroscopy (NIRS) sensor configurations and parameters. In this study, we developed a multilayer adult digital head model under both healthy and clinical settings and assessed light-tissue interaction through MC simulations in terms of partial differential pathlength, mean total optical pathlength, diffuse reflectance, detector light intensity and spatial sensitivity profile of optical measurements. The model incorporated four layers: scalp, skull, cerebrospinal-fluid and cerebral cortex with and without a customizable lesion for modeling hematoma of different sizes and depths. The effect of source-detector separation (SDS) on optical measurements' sensitivity to brain tissue was investigated. Results from 1330 separate simulations [(4 lesion volumes × 4 lesion depths for clinical +3 healthy settings) × 7 SDS × 10 simulation = 1330)] each with 100 million photons indicated that selection of SDS is critical to acquire optimal measurements from the brain and recommended SDS to be 25 to 35 mm depending on the wavelengths to obtain optical monitoring of the adult brain function. The findings here can guide the design of future NIRS probes for functional neuroimaging and clinical diagnostic systems.

摘要

理解近红外光在组织中的传播对于设计下一代光学脑成像设备至关重要。蒙特卡罗(MC)模拟为描述和评估各种近红外光谱(NIRS)传感器配置和参数的贡献提供了一种受控机制。在这项研究中,我们根据健康和临床环境开发了一个成人数字头颅的多层模型,并通过 MC 模拟评估了光与组织的相互作用,包括偏微分光程、平均总光程、漫反射、探测器光强和光学测量的空间灵敏度分布。该模型包含四个层次:头皮、颅骨、脑脊液和大脑皮层,以及用于模拟不同大小和深度血肿的可定制病变。研究了源-探测器分离(SDS)对脑组织光学测量灵敏度的影响。来自 1330 个单独模拟的结果[(临床+3 个健康设置的 4 个病变体积×4 个病变深度)×7 SDS×10 个模拟=1330)],每个模拟使用 1 亿个光子,表明 SDS 的选择对于从大脑中获得最佳测量至关重要,并建议根据波长选择 25 至 35mm 的 SDS,以实现对成人大脑功能的光学监测。本研究结果可为功能性神经成像和临床诊断系统的未来 NIRS 探头设计提供指导。

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