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首页> 美国卫生研究院文献>Biomedical Optics Express >Depth-resolved assessment of changes in concentration ofchromophores using time-resolved near-infrared spectroscopy:estimation of cytochrome-c-oxidase uncertainty by Monte Carlosimulations
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Depth-resolved assessment of changes in concentration ofchromophores using time-resolved near-infrared spectroscopy:estimation of cytochrome-c-oxidase uncertainty by Monte Carlosimulations

机译：深度解析的浓度变化评估使用时间分辨近红外光谱的生色团：蒙特卡洛法估算细胞色素-c-氧化酶的不确定度模拟

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Time-resolved near-infrared spectroscopy (TR-NIRS) measurements can be used to recover changes in concentrations of tissue constituents (ΔC) by applying the moments method and the Beer-Lambert law. In this work we carried out the error propagation analysis allowing to calculate the standard deviations of uncertainty in estimation of the ΔC. Here, we show the process of choosing wavelengths for the evaluation of hemodynamic (oxy-, deoxyhemoglobin) and metabolic (cytochrome-c-oxidase (CCO)) responses within the brain tissue as measured with an in-house developed TR-NIRS multi-wavelength system, which measures at 16 consecutive wavelengths separated by 12.5 nm and placed between 650 and 950 nm. Data generated with Monte Carlo simulations on three-layered model (scalp, skull, brain) for wavelengths range from 650 to 950 nm were used to carry out the error propagation analysis for varying choices of wavelengths. For a detector with a spectrally uniform responsivity, the minimal standard deviation of the estimated changes in CCO within the brain layer, σΔCCCObrain = 0.40 µM, was observed for the 16 consecutive wavelengths from 725 to912.5 nm. For realistic a detector model, i.e. the spectralresponsivity characteristic is considered, the minimum, σΔCCCObrain = 0.47µM, was observed at the 16 consecutive wavelengths from 688 to875 nm. We introduce the method of applying the errorpropagation analysis to data as measured with spectral TR-NIRS systemsto calculate uncertainty of recovery of tissue constituentsconcentrations.
机译：时间分辨近红外光谱（TR-NIRS）测量可用于恢复组织成分浓度的变化（ Δ C ），采用矩量法和比尔-兰伯特定律。在这项工作中，我们进行了误差传播分析，从而可以计算 Δ C 。在这里，我们展示了选择波长的过程，以评估使用内部开发的TR-NIRS多功能显示器测量的脑组织内的血流动力学（氧，脱氧血红蛋白）和代谢（细胞色素c-氧化酶（CCO））响应。波长系统，可测量16个连续波长，相隔12.5 nm，并位于650至950 nm之间。通过蒙特卡罗模拟在三层模型（头皮，头骨，大脑）上生成的波长范围为650至950 nm的数据被用于进行不同波长选择的误差传播分析。对于具有光谱均匀响应度的检测器，脑层内CCO估计变化的最小标准偏差， σ Δ C CCO 大脑 = 0.40 µM。912.5 nm。为了逼真的检测器模型，即光谱考虑到响应特性，最小值 σ Δ< / mo> C CCO brain = 0.47µM，在688至480的16个连续波长处观察到875 nm。我们介绍应用错误的方法使用光谱TR-NIRS系统对数据进行传播分析计算组织成分恢复的不确定性浓度。

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