Ms. Ran Li Profile

Ran Li

at Beijing Information Science and Technology Univ

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Proceedings Article | 18 January 2019 Paper

The studies of noninvasive blood glucose monitoring using optical coherence tomography

Ran Li, Guozhong Liu, Zhixiong Hu, Xiaopeng Xia, Tao Liu

Proceedings Volume 10839, 108390V (2019) https://doi.org/10.1117/12.2505124

KEYWORDS: Glucose, Blood, Scattering, Optical coherence tomography, Skin, Light scattering, Tissue optics, Tissues, Natural surfaces, Calibration

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World Health Organization(WHO) estimates that, there will be 300 million diabetics all over the world in 2025. The real-time monitoring and detecting of blood glucose level can prevent the occurrence of complications. Optical coherence tomography(OCT) provides high resolution images of the internal tissue structure based on the principle of low coherent interferometry. The change of glucose concentration in human blood mainly affects the scattering coefficient of the dermis layer. Therefore, The image of dermis layer of the finger skin tissue obtained by OCT to establish the relationship between the blood glucose concentration and the scattering coefficient. Due to the random thickness of various layers of the skin, the information of dermis layer can not be extracted accurately by average A-scan signals in B-scan image by aligning the skin surface. Therefore, the initial location of the dermis layer of each A-scan signal was accurately extracted by using image segmentation method. Based on the location of the dermis layer, all A-scan signals were aligned by dermis layer surface to obtain dermis layer information and scattering coefficients by averaging. The results of oral glucose tolerance test (OGTT) showed that, compared the method of extracting the dermis layer by aligning the dermis layer surface with aligning the skin surface, the correlation between the scattering coefficient and the blood glucose value was increased from 0.1778 to 0.8470, and the predicted error was decreased from 1.4359 to 0.6193. Therefore, it is better to predict the blood glucose value by accurately selecting the dermis layer , which is a new method for non-invasive blood glucose detecting.

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