| 引用本文: | 黄江茵,赵晶,董晓威,李颖婕.基于近红外光出射分布特性的膝骨性关节炎病程检测[J].生物信息学,2018,16(1):57-64. |
| HUANG Jiangyin,ZHAO Jing,DONG Xiaowei,LI Yingjie.Detection of knee osteoarthritis degree based on the distribution characteristics of exiting NIRS[J].Chinese Journal of Bioinformatics,2018,16(1):57-64. |
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| 摘要: |
| 膝骨性关节炎是中老年人群中常见的慢性、不可逆关节疾病。为了解决常规的CT扫描、核磁共振成像等检测手段存在的辐射影响较大,无法作为常规体检项目,以及无法检测出早期膝关节内部组织病变等缺点,本文提出了一种基于近红外光的无损、快速病程检测手段,结合临床膝关节CT图片用蒙特卡洛方法模拟红外光子在关节内部的运动轨迹,通过高斯函数分析和拟合不同病程下的出射光子分布特征,以有效光子出射率和拟合函数对称轴位置作为指标判定患者病情。该方法的优点在于,对人体不造成任何辐射损害,且能够通过计算机数据分析快速给出判定结果,可作为常规体检项目,便于发现早期病症并及时治疗。仿真实验结果表明该方法的准确率达到92%以上,在膝骨性关节炎的临床检测应用上具有较大的应用价值。 |
| 关键词: 膝骨性关节炎 近红外光 蒙特卡洛法 病程检测 高斯拟合 |
| DOI：10.3969/j.issn.1672-5565.201707004 |
| 分类号:R857.3 |
| 文献标识码:A |
| 基金项目:福建省科技厅自然科学基金(2017J9,5J01275)；福建省教育厅省属高校科研项目( JK2015034). |
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| Detection of knee osteoarthritis degree based on the distribution characteristics of exiting NIRS |
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HUANG Jiangyin, ZHAO Jing, DONG Xiaowei,LI Yingjie
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(Electrical Engineering and Automation Department, Xiamen University of Technology, Xiamen 361024,China)
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| Abstract: |
| Knee osteoarthritis is a common chronic and irreversible joint disease in the middle-aged and elderly population. Routine detection methods, such as computed tomography (CT) scans and MRI cannot be used as routine medical items because of radiation effects. Furthermore, most of these routine methods cannot detect the early internal lesions of the knee joint.To solve these problems, a non-destructive and rapid diagnostic method based on near infrared light is proposed. Combining the CT images of clinical knee joint, the motion paths of infrared photons in the joint were simulated by Monte Carlo method. The emission photon distribution characteristics under different diseases were analyzed and fitted by Gauss function. The effective photon ejection rate and the symmetry axis position of the fitting function are used as indexes to determine the patients condition. The advantage of this method is that it does not cause any radiation harm to the human body, and the detection result can be quickly given by computer data analysis. Therefore, this method can be used as a routine examination project to find early symptoms of knee OA, and give the patient prompt treatment. Simulation experimental results show that the accuracy of this method is more than 92%, and it is of great value in clinical application of knee osteoarthritis. |
| Key words: Knee osteoarthritis Near infrared light Monte Carlo method Disease detection Gaussian fitting |
