Research on <i>in-vivo</i> electron paramagnetic resonance spectrum classification and radiation dose prediction based on machine learning_Journal of Biomedical Engineering

Authors：

XIONG Guangwei ^1,2 , CHEN Bo ^1,2 , MA Lei ^1,3 , JIA Longpeng ^1,2 , CHEN Shunian ¹ , WU Ke ^1,3 , NING Jing ^1,3 ,  ZHU Bin ² ,  GUO Junwang ^1,3

1. Institute of Radiation Medicine, Academy of Military Medical Sciences, Academy of Military Sciences, Beijing 100850, P. R. China;
2. Institute of Smart Manufacturing Systems, Chang'an University, Xi'an 710061, P. R. China;
3. Beijing Key Laboratory of Radiobiology, Beijing 100850, P. R, China;

Corresponding?author：

ZHU Bin, Email: 412833157@qq.com; GUO Junwang, Email: guojunwang.thaa@vip.163.com

Keywords：

In-vivo electron paramagnetic resonance; Radiation dose; Spectrum classification; Support vector machine; Genetic algorithm to optimize neural network

DOI：

10.7507/1001-5515.202302015

Video：

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The in-vivo electron paramagnetic resonance (EPR) method can be used for on-site, rapid, and non-invasive 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.

Citation： XIONG Guangwei, CHEN Bo, MA Lei, JIA Longpeng, CHEN Shunian, WU Ke, NING Jing, ZHU Bin, GUO Junwang. Research on in-vivo electron paramagnetic resonance spectrum classification and radiation dose prediction based on machine learning. Journal of Biomedical Engineering, 2024, 41(5): 995-1002. doi: 10.7507/1001-5515.202302015 Copy

1.	郭俊旺. 輻射劑量在體測量電子順磁共振諧振腔研究. 北京: 中國人民解放軍軍事醫學科學院, 2015.
2.	袁清泉, 郭俊旺, 叢建波, 等. 在體劑量檢測專用EPR數據采集與控制系統. 波譜學雜志, 2013, 30(3): 354-360.
3.	Bordi F, Fattibene P, Onori S. ESR dose assessment in irradiated chicken legs. Radiation Physics and Chemistry, 1994, 43: 487-491.
4.	Chumak V, Pavlenko J, Sholom S. An approach to the assessment of overall uncertainty of determination of dose using an ESR technique. Applied Radiation and Isotopes, 1996, 47(11-12): 1287-1291.
5.	D’Oca M C, Bartolotta A. Evaluation of the original dose in irradiated dried fruit by EPR spectroscopy. Radiation Measurements, 2011, 46: 813-815.
6.	Zhumadilov K, Ivannikov A, Apsalikov K N, et al. Radiation dose estimation by tooth enamel EPR dosimetry for residents of dolon and bodene. Journal of Radiation Research, 2006, 47(Suppl A): A47-A53.
7.	Parlato A, Calderaro E, Bartolotta A, et al. Application of the ESR spectroscopy to estimate the original dose in irradiated chicken bone. Radiation Physics and Chemistry, 2007, 76: 1466-1469.
8.	Beshir W B. Identification and dose assessment of irradiated cardamom and cloves by EPR spectrometry. Radiation Physics and Chemistry, 2014, 96: 190-194.
9.	Kwona J H, Chung H W, Byun M W. ESR spectroscopy for detecting gamma-irradiated dried vegetables and estimating absorbed doses, Radiation Physics and Chemistry, 2000, 57: 319-324.
10.	Vanhaelewyn G, Callens F, Grun R. EPR spectrum deconvolution and dose assessment of fossil tooth enamel using maximum likelihood common factor analysis. Applied Radiation and Isotopes, 2000, 52: 1317-1326.
11.	Grün R, Ward K. A long-term fading study for ESR intensity measurement and dose evaluation on fossil tooth enamel. Radiation Measurements, 2002, 35: 269-274.
12.	Ivanov D V, Wieser A, Shishkina E A, et al. Effect of spectrum processing procedure on the linearity of EPR dose reconstruction in tooth enamel. Radiation Measurements, 2014, 68: 7-13.
13.	Boyau R J, Duval M, Bodin T. MCDoseE 2. 0 a new markov chain monte carlo program for ESR dose response curve fitting and dose evaluation. Quaternary Geochronology, 2018, 44: 13-22.
14.	Ciesielski B, Kaminska J, Emerich K. Analysis of various modifications in spectra analysis on accuracy of dose reconstructions in EPR dosimetry in tooth enamel. Radiation Measurements, 2011, 46: 783-788.
15.	Zou J, Guo J, Ma L, et al. A normalization method of the volume and geometry of tooth for X-band in vivo EPR dosimetry. Applied Radiation and Isotopes, 2019, 149: 123-129.
16.	鄒潔芮. 牙齒劑量電子順磁共振在體測量方法的實用性研究. 北京: 軍事科學院,2019.
17.	?ahiner E, Meri? N, Polymeris G S, et al. Impact of different mechanical pre-treatment to the EPR signals of human fingernails towards studying dose response and fading subjected to UV exposure or beta irradiation. Radiation Measurements, 2015, 82: 40-46.
18.	Zou J, Guo J, Dong G, et al. Effect of the tooth surface water on the accuracy of dose reconstructions in the X-band in vivo EPR dosimetry. Applied Radiation and Isotopes, 2018, 139: 86-90.
19.	Jiao L, Takada J, Endo S, et al. Effects of sunlight exposure on the human tooth enamel ESR spectra used for dose reconstruction. Journal of Radiation Research, 2007, 48(1): 21-29.
20.	Marciniak A, Ciesielski B, Prawdzik-Dampc A. The effect of dose and water treatment on EPR signals in irradiated fingernails. Radiation Protection Dosimetry, 2014, 162(1-2): 6-9.
21.	孫啟濤, 羅智孫, 梁好, 等. 基于多尺度特征提取的風機音頻信號故障診斷方法研究. 機電工程, 2023, 40(1): 39-46.
22.	丁世飛, 齊丙娟, 譚紅艷. 支持向量機理論與算法研究綜述. 電子科技大學學報, 2011, 40(1): 2-10.
23.	謝智東, 譚信, 袁昕旺, 等. 基于生成對抗數據增強支持向量機的小樣本信號調制識別算法. 電子與信息學報, 2023, 45(6): 2071-2080.
24.	杜海曼, 邊婷, 熊鵬, 等. 基于支持向量機多特征融合ST段形態分類. 生物醫學工程學雜志, 2022, 39(4): 702-712.
25.	Yang Mingjin, Zhou Wenju, Song Tianxiang. Audio-based fault diagnosis for belt conveyor rollers. Neurocomputing, 2020, 397: 447-456.
26.	崔建敏. 基于支持向量機的供暖系統智能診斷方法研究. 哈爾濱: 哈爾濱工業大學, 2021.
27.	石明, 汪舟, 甘進, 等. 基于GA-BP神經網絡的噴丸樣品表層硬度預測模型. 表面技術, 2022, 51(1): 332-338,357.
28.	于瑛, 陳笑, 賈曉宇, 等. 基于GA-BP神經網絡的逐時總輻射分組模型研究. 太陽能學報, 2022, 43(8): 157-163.
29.	黃體浩, 李俊青, 趙海勇. 遺傳算法優化的BP神經網絡拷貝數變異檢測. 計算機工程與應用, 2022, 58(1): 274-281.
30.	張曉俠, 劉鳳坤, 買巍, 等. 基于BP神經網絡及其改進算法的織機效率預測. 紡織學報, 2020, 41(8): 121-127.

1. 郭俊旺. 輻射劑量在體測量電子順磁共振諧振腔研究. 北京: 中國人民解放軍軍事醫學科學院, 2015.
2. 袁清泉, 郭俊旺, 叢建波, 等. 在體劑量檢測專用EPR數據采集與控制系統. 波譜學雜志, 2013, 30(3): 354-360.
3. Bordi F, Fattibene P, Onori S. ESR dose assessment in irradiated chicken legs. Radiation Physics and Chemistry, 1994, 43: 487-491.
4. Chumak V, Pavlenko J, Sholom S. An approach to the assessment of overall uncertainty of determination of dose using an ESR technique. Applied Radiation and Isotopes, 1996, 47(11-12): 1287-1291.
5. D’Oca M C, Bartolotta A. Evaluation of the original dose in irradiated dried fruit by EPR spectroscopy. Radiation Measurements, 2011, 46: 813-815.
6. Zhumadilov K, Ivannikov A, Apsalikov K N, et al. Radiation dose estimation by tooth enamel EPR dosimetry for residents of dolon and bodene. Journal of Radiation Research, 2006, 47(Suppl A): A47-A53.
7. Parlato A, Calderaro E, Bartolotta A, et al. Application of the ESR spectroscopy to estimate the original dose in irradiated chicken bone. Radiation Physics and Chemistry, 2007, 76: 1466-1469.
8. Beshir W B. Identification and dose assessment of irradiated cardamom and cloves by EPR spectrometry. Radiation Physics and Chemistry, 2014, 96: 190-194.
9. Kwona J H, Chung H W, Byun M W. ESR spectroscopy for detecting gamma-irradiated dried vegetables and estimating absorbed doses, Radiation Physics and Chemistry, 2000, 57: 319-324.
10. Vanhaelewyn G, Callens F, Grun R. EPR spectrum deconvolution and dose assessment of fossil tooth enamel using maximum likelihood common factor analysis. Applied Radiation and Isotopes, 2000, 52: 1317-1326.
11. Grün R, Ward K. A long-term fading study for ESR intensity measurement and dose evaluation on fossil tooth enamel. Radiation Measurements, 2002, 35: 269-274.
12. Ivanov D V, Wieser A, Shishkina E A, et al. Effect of spectrum processing procedure on the linearity of EPR dose reconstruction in tooth enamel. Radiation Measurements, 2014, 68: 7-13.
13. Boyau R J, Duval M, Bodin T. MCDoseE 2. 0 a new markov chain monte carlo program for ESR dose response curve fitting and dose evaluation. Quaternary Geochronology, 2018, 44: 13-22.
14. Ciesielski B, Kaminska J, Emerich K. Analysis of various modifications in spectra analysis on accuracy of dose reconstructions in EPR dosimetry in tooth enamel. Radiation Measurements, 2011, 46: 783-788.
15. Zou J, Guo J, Ma L, et al. A normalization method of the volume and geometry of tooth for X-band in vivo EPR dosimetry. Applied Radiation and Isotopes, 2019, 149: 123-129.
16. 鄒潔芮. 牙齒劑量電子順磁共振在體測量方法的實用性研究. 北京: 軍事科學院,2019.
17. ?ahiner E, Meri? N, Polymeris G S, et al. Impact of different mechanical pre-treatment to the EPR signals of human fingernails towards studying dose response and fading subjected to UV exposure or beta irradiation. Radiation Measurements, 2015, 82: 40-46.
18. Zou J, Guo J, Dong G, et al. Effect of the tooth surface water on the accuracy of dose reconstructions in the X-band in vivo EPR dosimetry. Applied Radiation and Isotopes, 2018, 139: 86-90.
19. Jiao L, Takada J, Endo S, et al. Effects of sunlight exposure on the human tooth enamel ESR spectra used for dose reconstruction. Journal of Radiation Research, 2007, 48(1): 21-29.
20. Marciniak A, Ciesielski B, Prawdzik-Dampc A. The effect of dose and water treatment on EPR signals in irradiated fingernails. Radiation Protection Dosimetry, 2014, 162(1-2): 6-9.
21. 孫啟濤, 羅智孫, 梁好, 等. 基于多尺度特征提取的風機音頻信號故障診斷方法研究. 機電工程, 2023, 40(1): 39-46.
22. 丁世飛, 齊丙娟, 譚紅艷. 支持向量機理論與算法研究綜述. 電子科技大學學報, 2011, 40(1): 2-10.
23. 謝智東, 譚信, 袁昕旺, 等. 基于生成對抗數據增強支持向量機的小樣本信號調制識別算法. 電子與信息學報, 2023, 45(6): 2071-2080.
24. 杜海曼, 邊婷, 熊鵬, 等. 基于支持向量機多特征融合ST段形態分類. 生物醫學工程學雜志, 2022, 39(4): 702-712.
25. Yang Mingjin, Zhou Wenju, Song Tianxiang. Audio-based fault diagnosis for belt conveyor rollers. Neurocomputing, 2020, 397: 447-456.
26. 崔建敏. 基于支持向量機的供暖系統智能診斷方法研究. 哈爾濱: 哈爾濱工業大學, 2021.
27. 石明, 汪舟, 甘進, 等. 基于GA-BP神經網絡的噴丸樣品表層硬度預測模型. 表面技術, 2022, 51(1): 332-338,357.
28. 于瑛, 陳笑, 賈曉宇, 等. 基于GA-BP神經網絡的逐時總輻射分組模型研究. 太陽能學報, 2022, 43(8): 157-163.
29. 黃體浩, 李俊青, 趙海勇. 遺傳算法優化的BP神經網絡拷貝數變異檢測. 計算機工程與應用, 2022, 58(1): 274-281.
30. 張曉俠, 劉鳳坤, 買巍, 等. 基于BP神經網絡及其改進算法的織機效率預測. 紡織學報, 2020, 41(8): 121-127.

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