Cohen David Jonathan Fulop, Li Natalie C, Ioussoufovitch Seva, Diop Mamadou
Department of Medical Biophysics, Western University, London, ON, Canada.
School of Biomedical Engineering, Western University, London, ON, Canada.
Front Neurosci. 2023 Feb 16;17:1020151. doi: 10.3389/fnins.2023.1020151. eCollection 2023.
Near-infrared spectroscopy (NIRS) can measure tissue blood content and oxygenation; however, its use for adult neuromonitoring is challenging due to significant contamination from their thick extracerebral layers (ECL; primarily scalp and skull). This report presents a fast method for accurate estimation of adult cerebral blood content and oxygenation from hyperspectral time resolved NIRS (trNIRS) data. A two-phase fitting method, based on a two-layer head model (ECL and brain), was developed. Phase 1 uses spectral constraints to accurately estimate the baseline blood content and oxygenation in both layers, which are then used by Phase 2 to correct for the ECL contamination of the late-arriving photons. The method was validated with data from Monte-Carlo simulations of hyperspectral trNIRS in a realistic model of the adult head obtained from a high-resolution MRI. Phase 1 recovered cerebral blood oxygenation and total hemoglobin with an accuracy of 2.7 ± 2.5 and 2.8 ± 1.8%, respectively, with unknown ECL thickness, and 1.5 ± 1.4 and 1.7 ± 1.1% when the ECL thickness was known. Phase 2 recovered these parameters with an accuracy of 1.5 ± 1.5 and 3.1 ± 0.9%, respectively. Future work will include further validation in tissue-mimicking phantoms with various top layer thicknesses and in a pig model of the adult head before human applications.
近红外光谱技术（NIRS）能够测量组织的血液含量和氧合情况；然而，由于成人较厚的脑外层（ECL，主要是头皮和颅骨）会产生显著干扰，将其用于成人神经监测具有挑战性。本报告提出了一种从高光谱时间分辨近红外光谱（trNIRS）数据中准确估算成人大脑血液含量和氧合情况的快速方法。我们开发了一种基于双层头部模型（ECL和大脑）的两阶段拟合方法。第一阶段利用光谱约束来准确估算两层中的基线血液含量和氧合情况，然后第二阶段利用这些估算值来校正迟发光子的ECL干扰。该方法通过对从高分辨率MRI获得的成人头部真实模型中的高光谱trNIRS进行蒙特卡罗模拟得到的数据进行了验证。在ECL厚度未知的情况下，第一阶段恢复大脑血液氧合和总血红蛋白的准确率分别为2.7±2.5%和2.8±1.8%，在ECL厚度已知时，准确率分别为1.5±1.4%和1.7±1.1%。第二阶段恢复这些参数的准确率分别为1.5±1.5%和3.1±0.9%。未来的工作将包括在具有各种顶层厚度的组织模拟体模以及成人头部猪模型中进行进一步验证，然后再应用于人体。
