基于机器学习的在体电子顺磁共振波谱分类和辐射剂量预测方法研究

Research on in-vivo electron paramagnetic resonance spectrum classification and radiation dose prediction based on machine learning

核与辐射突发事件发生后,在体电子顺磁共振(in-vivo EPR)方法可对伤员辐射剂量进行现场、快速、无创检测。对in-vivo EPR波谱分析,目前常采用人工标记峰值并计算信号强度,存在工作量大、受主观因素干扰等问题。本研究利用支持向量机(SVM)技术,建立了一种对in-vivo EPR波谱进行自动分类识别的方法,可批量自动识别并筛除in-vivo EPR测量时因振动、牙表面水干扰而产生的无效波谱。本研究利用遗传算法优化神经网络(GA-BPNN)建立了一种波谱分析方法,可对in-vivo EPR波谱中的辐射诱发信号进行自动识别,并预测伤员受到辐射的剂量。实验结果表明,本研究建立的SVM和GA-BPNN波谱处理方法可有效地完成in-vivo EPR波谱自动分类和剂量预测,可满足核事故应急剂量评估的需求。本研究探索了机器学习方法在电子顺磁共振(EPR)波谱处理领域的应用,提高了EPR波谱处理的智能化水平,为提升大批量EPR波谱处理效率提供了支撑。

The in-vivo electron paramagnetic resonance(EPR)method can be used for on-site,rapid,and noninvasive detection of radiation dose to casualties after nuclear and radiation emergencies.For in-vivo EPR spectrum analysis,manual labeling of peaks and calculation of signal intensity are often used,which have problems such as large workload and interference by subjective factors.In this study,a method for automatic classification and identification of in-vivo EPR spectra was established using support vector machine(SVM)technology,which can in-batch and automatically identify and screen out invalid spectra due to vibration and dental surface water interference during in-vivo EPR measurements.In this study,a spectrum analysis method based on genetic algorithm optimization neural network(GA-BPNN)was established,which can automatically identify the radiation-induced signals in in-vivo EPR spectra and predict the radiation doses received by the injured.The experimental results showed that the SVM and GA-BPNN spectrum processing methods established in this study could effectively accomplish the automatic spectra classification and radiation dose prediction,and could meet the needs of dose assessment in nuclear emergency.This study explored the application of machine learning methods in EPR spectrum processing,improved the intelligence level of EPR spectrum processing,and would help to enhance the efficiency of mass EPR spectra processing.