Application of electrical impedance tomography imaging technology combined with generative adversarial network in pulmonary ventilation monitoring_Journal of Biomedical Engineering

Authors：

 ZHAO Mingkang ^1,2,3 , LIU Jun ^1,2,3 , GUO Zhongsheng ^2,3 , CHEN Xiangqi ^1,2,3 , ZHANG Shuai ^1,2,3 , ZHENG Tianyu ²

1. School of Health Sciences & Biomedical Engineering, Hebei University of Technology, Tianjin 300130, P. R. China;
2. State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, P. R. China;
3. Tianjin Key Laboratory of Bioelectricity and Intelligent Health, Tianjin 300130, P. R. China;

Corresponding author：

ZHAO Mingkang, Email: mingkangsky@hebut.edu.cn

Keywords：

Electrical impedance tomography; Kalman filter; Generative adversarial network; Image reconstruction

DOI：

10.7507/1001-5515.202308026

Video：

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Electrical impedance tomography (EIT) plays a crucial role in the monitoring of pulmonary ventilation and regional pulmonary function test. However, the inherent ill-posed nature of EIT algorithms results in significant deviations in the reconstructed conductivity obtained from voltage data contaminated with noise, making it challenging to obtain accurate distribution images of conductivity change as well as clear boundary contours. In order to enhance the image quality of EIT in lung ventilation monitoring, a novel approach integrating the EIT with deep learning algorithm was proposed. Firstly, an optimized operator was introduced to enhance the Kalman filter algorithm, and Tikhonov regularization was incorporated into the state-space expression of the algorithm to obtain the initial lung image reconstructed. Following that, the imaging outcomes were fed into a generative adversarial network model in order to reconstruct accurate lung contours. The simulation experiment results indicate that the proposed method produces pulmonary images with clear boundaries, demonstrating increased robustness against noise interference. This methodology effectively achieves a satisfactory level of visualization and holds potential significance as a reference for the diagnostic purposes of imaging modalities such as computed tomography.

Citation： ZHAO Mingkang, LIU Jun, GUO Zhongsheng, CHEN Xiangqi, ZHANG Shuai, ZHENG Tianyu. Application of electrical impedance tomography imaging technology combined with generative adversarial network in pulmonary ventilation monitoring. Journal of Biomedical Engineering, 2024, 41(1): 105-113. doi: 10.7507/1001-5515.202308026 Copy

1.	叶健安, 田翔, 王普, 等. 基于多分支一维卷积神经网络电阻抗断层成像数据后处理方法研究. 空军军医大学学报, 2022, 43(7): 842-846,851.
2.	田翠杰, 王海播, 张文平, 等. 电阻抗断层成像技术在呼吸系统疾病中的临床应用. 中国呼吸与危重监护杂志, 2020, 19(6): 617-620.
3.	Barber D C. A review of image reconstruction techniques for electrical impedance tomography. Med Phys, 1989, 16(2): 162-169.
4.	Widodo A, Endarko. Experimental study of one step linear Gauss-Newton algorithm for improving the quality of image reconstruction in high-speed electrical impedance tomography. J Phys Conf Ser, 2018, 1120: 012067.
5.	Vauhkonen M, Karjalainen P A, Kaipio J P. A Kalman filter approach to track fast impedance changes in electrical impedance tomography. IEEE Trans Biomed Eng, 1998, 45(4): 486-493.
6.	Wang H, Yang Y Y, Pan Y, et al. Detecting thoracic diseases via representation learning with adaptive sampling. Neurocomputing, 2020, 406: 354-360.
7.	Wang Y, Yu B, Wang L, et al. 3D conditional generative adversarial networks for high-quality PET image estimation at low dose. Neuroimage, 2018, 174: 550-562.
8.	Chen Y, Yang X H, Wei Z, et al. Generative adversarial networks in medical image augmentation: a review. Comput Biol Med, 2022, 144: 105382.
9.	Faruqui N, Yousuf M A, Whaiduzzaman M, et al. LungNet: a hybrid deep-CNN model for lung cancer diagnosis using CT and wearable sensor-based medical IoT data. Comput Biol Med, 2021, 139: 104961.
10.	Chan H P, Hadjiiski L M, Samala R K. Computer-aided diagnosis in the era of deep learning. Med Phys, 2020, 47(5): e218-e227.
11.	Goodfellow I J, Pouget-Abadie J, Mirza M, et al. Generative adversarial nets// Proceedings of the 27th International Conference on Neural Information Processing Systems (NIPS’14), Montreal: NIPS, 2014, 2: 2672-2680.
12.	李佳.电阻抗层析成像的数据融合与正则化算法研究. 天津: 天津大学, 2020.
13.	Liu D, Smyl D, Du J. Nonstationary shape estimation in electrical impedance tomography using a parametric level set-based extended Kalman filter approach. IEEE Transactions on Instrumentation and Measurement, 2019, 69(5): 1894-1907.
14.	Kim K Y, Kim B, Kim M C, et al. Image reconstruction in time-varying electrical impedance tomography based on the extended Kalman filter. Measurement Science and Technology, 2001, 12(8): 1032.
15.	Zhang J, Zhang L, Liu Z, et al. Tikhonov regularization-based extended Kalman filter technique for robust and accurate reconstruction in diffuse optical tomography. J Opt Soc Am A Opt Image Sci Vis, 2023, 40(1): 10-20.
16.	Qiu Y, Zhao H. Ocean acoustic tomography with moving node based on Tikhonov regularized Kalman filter. Journal of Physics: Conference Series, 2019, 1169(1): 012027.
17.	Carmi A, Gurfil P, Kanevsky D. Methods for sparse signal recovery using Kalman filtering with embedded pseudo-measurement norms and quasi-norms. IEEE Transactions on Signal Processing, 2010, 58(4): 2405-2409.
18.	Li X, Zhang R, Wang Q, et al. SAR-CGAN: improved generative adversarial network for EIT reconstruction of lung diseases. Biomedical Signal Processing and Control, 2023, 81: 104421.
19.	Garehdaghi F, Meshgini S, Afrouzian R. Positron emission tomography image enhancement using magnetic resonance images and U-net structure. Computers & Electrical Engineering, 2021, 90: 106973.
20.	Isola P, Zhu J Y, Zhou T, et al. Image-to-image translation with conditional adversarial networks//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017: 1125-1134.
21.	Liao F, Liang M, Li Z, et al. Evaluate the malignancy of pulmonary nodules using the 3-D deep leaky noisy-OR network. IEEE Trans Neural Netw Learn Syst, 2019, 30(11): 3484-3495.
22.	Petousis P, Han S X, Aberle D, et al. Prediction of lung cancer incidence on the low-dose computed tomography arm of the national lung screening trial: a dynamic Bayesian network. Artif Intell Med, 2016, 72: 42-55.
23.	Kiser K J, Ahmed S, Stieb S, et al. PleThora: pleural effusion and thoracic cavity segmentations in diseased lungs for benchmarking chest CT processing pipelines. Med Phys, 2020, 47(11): 5941-5952.
24.	Wang H, Hu L, Wang J, et al. An image reconstruction algorithm of EIT based on pulmonary prior information. Frontiers of Electrical and Electronic Engineering in China, 2009, 4(2): 121-126.
25.	Li X, Chen X, Wang Q, et al. Electrical impedance tomography imaging based on a new three-dimensional thorax model for assessing the extent of lung injury. AIP Advances, 2019, 9(12): 125310.

1. 叶健安, 田翔, 王普, 等. 基于多分支一维卷积神经网络电阻抗断层成像数据后处理方法研究. 空军军医大学学报, 2022, 43(7): 842-846,851.
2. 田翠杰, 王海播, 张文平, 等. 电阻抗断层成像技术在呼吸系统疾病中的临床应用. 中国呼吸与危重监护杂志, 2020, 19(6): 617-620.
3. Barber D C. A review of image reconstruction techniques for electrical impedance tomography. Med Phys, 1989, 16(2): 162-169.
4. Widodo A, Endarko. Experimental study of one step linear Gauss-Newton algorithm for improving the quality of image reconstruction in high-speed electrical impedance tomography. J Phys Conf Ser, 2018, 1120: 012067.
5. Vauhkonen M, Karjalainen P A, Kaipio J P. A Kalman filter approach to track fast impedance changes in electrical impedance tomography. IEEE Trans Biomed Eng, 1998, 45(4): 486-493.
6. Wang H, Yang Y Y, Pan Y, et al. Detecting thoracic diseases via representation learning with adaptive sampling. Neurocomputing, 2020, 406: 354-360.
7. Wang Y, Yu B, Wang L, et al. 3D conditional generative adversarial networks for high-quality PET image estimation at low dose. Neuroimage, 2018, 174: 550-562.
8. Chen Y, Yang X H, Wei Z, et al. Generative adversarial networks in medical image augmentation: a review. Comput Biol Med, 2022, 144: 105382.
9. Faruqui N, Yousuf M A, Whaiduzzaman M, et al. LungNet: a hybrid deep-CNN model for lung cancer diagnosis using CT and wearable sensor-based medical IoT data. Comput Biol Med, 2021, 139: 104961.
10. Chan H P, Hadjiiski L M, Samala R K. Computer-aided diagnosis in the era of deep learning. Med Phys, 2020, 47(5): e218-e227.
11. Goodfellow I J, Pouget-Abadie J, Mirza M, et al. Generative adversarial nets// Proceedings of the 27th International Conference on Neural Information Processing Systems (NIPS’14), Montreal: NIPS, 2014, 2: 2672-2680.
12. 李佳.电阻抗层析成像的数据融合与正则化算法研究. 天津: 天津大学, 2020.
13. Liu D, Smyl D, Du J. Nonstationary shape estimation in electrical impedance tomography using a parametric level set-based extended Kalman filter approach. IEEE Transactions on Instrumentation and Measurement, 2019, 69(5): 1894-1907.
14. Kim K Y, Kim B, Kim M C, et al. Image reconstruction in time-varying electrical impedance tomography based on the extended Kalman filter. Measurement Science and Technology, 2001, 12(8): 1032.
15. Zhang J, Zhang L, Liu Z, et al. Tikhonov regularization-based extended Kalman filter technique for robust and accurate reconstruction in diffuse optical tomography. J Opt Soc Am A Opt Image Sci Vis, 2023, 40(1): 10-20.
16. Qiu Y, Zhao H. Ocean acoustic tomography with moving node based on Tikhonov regularized Kalman filter. Journal of Physics: Conference Series, 2019, 1169(1): 012027.
17. Carmi A, Gurfil P, Kanevsky D. Methods for sparse signal recovery using Kalman filtering with embedded pseudo-measurement norms and quasi-norms. IEEE Transactions on Signal Processing, 2010, 58(4): 2405-2409.
18. Li X, Zhang R, Wang Q, et al. SAR-CGAN: improved generative adversarial network for EIT reconstruction of lung diseases. Biomedical Signal Processing and Control, 2023, 81: 104421.
19. Garehdaghi F, Meshgini S, Afrouzian R. Positron emission tomography image enhancement using magnetic resonance images and U-net structure. Computers & Electrical Engineering, 2021, 90: 106973.
20. Isola P, Zhu J Y, Zhou T, et al. Image-to-image translation with conditional adversarial networks//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017: 1125-1134.
21. Liao F, Liang M, Li Z, et al. Evaluate the malignancy of pulmonary nodules using the 3-D deep leaky noisy-OR network. IEEE Trans Neural Netw Learn Syst, 2019, 30(11): 3484-3495.
22. Petousis P, Han S X, Aberle D, et al. Prediction of lung cancer incidence on the low-dose computed tomography arm of the national lung screening trial: a dynamic Bayesian network. Artif Intell Med, 2016, 72: 42-55.
23. Kiser K J, Ahmed S, Stieb S, et al. PleThora: pleural effusion and thoracic cavity segmentations in diseased lungs for benchmarking chest CT processing pipelines. Med Phys, 2020, 47(11): 5941-5952.
24. Wang H, Hu L, Wang J, et al. An image reconstruction algorithm of EIT based on pulmonary prior information. Frontiers of Electrical and Electronic Engineering in China, 2009, 4(2): 121-126.
25. Li X, Chen X, Wang Q, et al. Electrical impedance tomography imaging based on a new three-dimensional thorax model for assessing the extent of lung injury. AIP Advances, 2019, 9(12): 125310.

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