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光在宫颈组织中传输特性的蒙特卡罗模拟

Study on the light transport properties in cervical tissue by Monte Carlo Method

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【作者】 钟旻张丹钱志余

【Author】 ZHONG Min;ZHANG Dan;QIAN Zhiyu;School of Automation, Nanjing University of Aeronautics and Astronautics;

【通讯作者】 钱志余;

【机构】 南京航空航天大学自动化学院

【摘要】 为研究可见近红外光谱在宫颈组织中的光子传输情况,根据宫颈组织的结构,构建双层宫颈组织模型,利用蒙特卡罗方法模拟可见光(500nm左右)和近红外光(810nm左右)在宫颈组织中的光分布,研究宫颈正常组织和高级病变组织中漫反射率、吸收能量密度、光能流率的变化的差异。结果表明:宫颈正常组织表面漫反射率大于高级病变组织;在上皮层中宫颈正常组织光吸收能量密度大于高级病变组织,基质层宫颈正常组织光吸收能量密度小于高级病变组织;宫颈正常组织的光能流率大于高级病变组织,与可见光相比,近红外光在宫颈组织中的分布范围更大、探测深度更深。为使用可见近红外光谱技术进行宫颈癌前病变诊断提供了一定的理论基础。

【Abstract】 In order to study the photon transport of visible near-infrared spectroscopy in cervical tissue, a doublelayer cervical tissue model was constructed according to the structure of cervical tissue, and the Monte Carlo method was used to simulate the light distribution of visible light(about 500 nm) and near-infrared light(about 810 nm) in cervical tissue, and the differences in diffuse reflectance, absorbed energy density and light energy flow rate in normal cervical tissue and high-grade precancer tissue were studied. The results showed that the diffuse reflectance of normal cervical tissue was greater than that of high-grade diseased tissue. In the epithelial layer, the light absorption energy density of normal cervical tissues is greater than that of high-grade diseased tissues, and the light-absorbed energy density of normal cervical tissues in the stromal layer is smaller than that of high-grade diseased tissues. The light energy flow rate of normal cervical tissues is greater than that of high-grade diseased tissues, and compared with visible light, NIR light has a greater range and deeper detection depth in cervical tissue. It provides a theoretical basis for the diagnosis of cervical precancerous lesions using visible near-infrared spectroscopy.

【基金】 国家自然科学基金重大科研仪器研制项目(81827803,81727804);国家自然科学基金指南引导类原创探索计划项目(82151311)国家自然科学基金(61875085,11902154);江苏省重点研发计划(社会发展)项目(BE2020705);江苏省自然科学基金(BK20190387)
  • 【文献出处】 生命科学仪器 ,Life Science Instruments , 编辑部邮箱 ,2023年01期
  • 【分类号】R737.33
  • 【下载频次】11
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