Photonics Information Innovation Center, Hebei Provincial Center for Optical Sensing Innovations, College of Physics Science & Technology, Hebei University, Wusidonglu NO. 180, Baoding, 071002, China.
Affiliated Hospital, Hebei University, Baoding, China.
Lasers Med Sci. 2021 Oct;36(8):1649-1659. doi: 10.1007/s10103-021-03244-x. Epub 2021 Feb 1.
The optical scattering coefficient (μ) in the dermis layer of human skin obtained with optical coherence tomography (OCT) has shown to have a strong correlation with the blood glucose concentration (BGC), which can be used for noninvasive BGC monitoring. Unfortunately, the nonhomogeneity in the skin may cause inaccuracies for the BGC analysis. In this paper, we propose a 2D correlation analysis method to identify 2D regions in the skin with μ sensitive to BGC variations and only use data in these regions to calculate μ for minimizing the inaccuracy induced by nonhomogeneity and therefore improving the accuracy of OCT-based BGC monitoring. We demonstrate the effectiveness of the 2D method with OCT data obtained with in vivo human forearm skins of nine different human subjects. In particular, we present a 3D OCT data set in a two-dimensional (2D) map of depth vs. a lateral dimension and calculate the correlation coefficient R between the μ and the BGC in each region of the 2D map with the BGC data measured with a glucose meter using finger blood. We filter out the μ data from regions with low R values and only keep the μ data with R values sufficiently high (R-filter). The filtered μ data in all the regions are then averaged to produce an average μ data. We define a term called overall relevancy (OR) to quantify the degree of correlation between the filtered/averaged μ data and the finger-blood BGC data to determine the optimal R value for such an R-filter with the highest obtained OR. We found that the optimal R for such an R-filter has an absolute value (|R|) of 0.6 or 0.65. We further show that the R-filter obtained with the 2D correlation method yields better OR between μ and the BGC than that obtained with the previously reported 1D correlation method. We believe that the method demonstrated in this paper is important for understanding the influence of BGC on μ in human skins and therefore for improving the accuracy of OCT-based noninvasive BGC monitoring, although further studies are required to validate its effectiveness.
利用光学相干断层扫描（OCT）获得的人皮肤真皮层的光散射系数（μ）与血糖浓度（BGC）有很强的相关性，可用于非侵入性 BGC 监测。不幸的是，皮肤的非均质性可能会导致 BGC 分析不准确。在本文中，我们提出了一种 2D 相关分析方法，以识别对 BGC 变化敏感的皮肤 2D 区域，并仅使用这些区域的数据来计算 μ，以最小化非均质性引起的误差，从而提高基于 OCT 的 BGC 监测的准确性。我们使用来自九名不同人类受试者的体内前臂皮肤的 OCT 数据证明了 2D 方法的有效性。特别是，我们提出了一个 3D OCT 数据集，在一个二维（2D）深度与横向尺寸的映射中，并使用血糖仪测量的手指血中的 BGC 数据计算 2D 映射中每个区域的 μ与 BGC 之间的相关系数 R。我们从低 R 值的区域中过滤出 μ 数据，并仅保留 R 值足够高（R 过滤）的 μ 数据。然后将所有区域的过滤后的 μ 数据平均以产生平均 μ 数据。我们定义了一个称为整体相关性（OR）的术语来量化过滤/平均 μ 数据与手指血 BGC 数据之间的相关性程度，以确定用于这种 R 过滤的最佳 R 值，以获得最高的 OR。我们发现，这种 R 过滤的最佳 R 值的绝对值（|R|）为 0.6 或 0.65。我们进一步表明，与先前报道的 1D 相关方法相比，使用 2D 相关方法获得的 R 过滤可获得更好的 μ 与 BGC 之间的 OR。我们相信，本文所展示的方法对于理解 BGC 对人皮肤中 μ 的影响非常重要，因此可以提高基于 OCT 的非侵入性 BGC 监测的准确性，尽管还需要进一步的研究来验证其有效性。
