Image reconstruction for cerebral hemorrhage based on improved densely-connected fully convolutional neural network_Journal of Biomedical Engineering

Authors：

SHI Yanyan ^1,2 , WANG Luanjun ¹ , LI Yating ¹ ,  WANG Meng ¹ , YANG Bin ² , FU Feng ²

1. School of Electronic and Electrical Engineering, Henan Normal University, Xinxiang, Henan 453000, P. R. China;
2. Faculty of Biomedical Engineering, Fourth Military Medical University, Xi’an 710032, P. R. China;

Corresponding?author：

WANG Meng, Email: wangmeng@htu.edu.cn

Keywords：

Electrical impedance tomography; Cerebral hemorrhage; Image reconstruction; Neural network

DOI：

10.7507/1001-5515.202406044

Video：

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Abstract Full text Figures/Tables Video References Cited by

Cerebral hemorrhage is a serious cerebrovascular disease with high morbidity and high mortality, for which timely diagnosis and treatment are crucial. Electrical impedance tomography (EIT) is a functional imaging technique which is able to detect abnormal changes of electrical property of the brain tissue at the early stage of the disease. However, irregular multi-layer structure and different conductivity properties of each layer affect image reconstruction of the brain EIT, resulting in low reconstruction quality. To solve this problem, an image reconstruction method based on an improved densely-connected fully convolutional neural network is proposed in this paper. On the basis of constructing a three-layer cerebral model that approximates the real structure of the human head, the nonlinear mapping between the boundary voltage and the conductivity change is determined by network training, which avoids the error caused by the traditional sensitivity matrix method used for solving inverse problem. The proposed method is also evaluated under the conditions with or without noise, as well as with brain model change. The numerical simulation and phantom experimental results show that conductivity distribution of cerebral hemorrhage can be accurately reconstructed with the proposed method, providing a reliable basis for the diagnosis and treatment of cerebral hemorrhage. Also, it promotes the application of EIT in the diagnosis of brain diseases.

Citation： SHI Yanyan, WANG Luanjun, LI Yating, WANG Meng, YANG Bin, FU Feng. Image reconstruction for cerebral hemorrhage based on improved densely-connected fully convolutional neural network. Journal of Biomedical Engineering, 2024, 41(6): 1185-1194. doi: 10.7507/1001-5515.202406044 Copy

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2.	張敏敏, 吳濤, 吳雄楓, 等. 神經內鏡手術對幕上高血壓性腦出血患者的療效分析: 一項單中心回顧性病例對照研究. 海軍軍醫大學學報, 2024, 45(4): 421-426.
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4.	Rindler R S, Allen J W, Barrow J W, et al. Neuroimaging of intracerebral hemorrhage. Neurosurgery, 2020, 86(5): E414-E423.
5.	楊琳, 代萌, 徐燦華, 等. 正常腦組織和腦卒中組織的電阻抗頻譜特性研究. 醫療衛生裝備, 2019, 40(2): 16-20,35.
6.	王緩, 鄭欣, 鐘鳴. 急性腦損傷并發ARDS患者中乳酸水平與EIT中背側通氣分布關聯性的臨床研究. 中國臨床新醫學, 2024, 17(2): 145-150.
7.	李穎. 基于組織系數的生物組織電特性分析方法研究. 醫療衛生裝備, 2022, 43(5): 8-13.
8.	白世展, 李文勝, 林海軍, 等. 基于徑向基函數神經網絡的肺部加權頻差電阻抗成像方法. 生物化學與生物物理進展, 2023, 50(7): 1755-1766.
9.	趙明康, 劉珺, 郭忠圣, 等. 結合生成對抗網絡的電阻抗斷層成像技術在肺通氣監測中的應用. 生物醫學工程學雜志, 2024, 41(1): 105-113.
10.	張濤, 安志偉, 劉學超, 等. 腦部電阻抗有限元模型的腦膜結構仿真. 醫療衛生裝備, 2020, 41(4): 35-38.
11.	劉凱, 李安琪, 李芳, 等. 基于三維電阻抗層析成像的乳腺癌傳感器的設計. 分析化學, 2024, 52(2): 248-258.
12.	徐德洪, 鄧琪, 姚佳烽. 基于電阻抗成像技術的細胞檢測傳感器開發. 傳感器與微系統, 2023, 42(11): 93-95, 104.
13.	Wang H, Liu K, Wu Y, et al. Image reconstruction for electrical impedance tomography using radial basis function neural network based on hybrid particle swarm optimization algorithm. IEEE Sensors Journal, 2021, 21(2): 1926-1934.
14.	Sun B, Yue S, Hao Z, et al. An improved tikhonov regularization method for lung cancer monitoring using electrical impedance tomography. IEEE Sensors Journal, 2019, 19(8): 3049-3057.
15.	Lee K, Woo E J, Seo J K. A fidelity-embedded regularization method for robust electrical impedance tomography. IEEE Trans Med Imaging, 2018, 37(9): 1970-1977.
16.	Liu X, Li H, Ma H, et al. An iterative damped least-squares algorithm for simultaneously monitoring the development of hemorrhagic and secondary ischemic lesions in brain injuries. Med Biol Eng Comput, 2019, 57(9): 1917-1931.
17.	Shi Y, Wu Y, Wang M, et al. Sparse image reconstruction of intracerebral hemorrhage with electrical impedance tomography. J Med Imaging, 2021, 8(1): 014501.
18.	Xiang J, Dong Y, Yang Y. FISTA-Net: learning a fast iterative shrinkage thresholding network for inverse problems in imaging. IEEE Trans Med Imaging, 2021, 40(5): 1329-1339.
19.	Hamilton S J, Hanninen A, Hauptmann A, et al. Beltrami-net: domain-independent deep D-bar learning for absolute imaging with electrical impedance tomography (a-EIT). Physiol Meas, 2019, 40(7): 074002.
20.	Li X, Zhang R, Wang Q, et al. SAR-CGAN: improved generative adversarial network for EIT reconstruction of lung diseases. Biomed Signal Process Control, 2023, 81: 104421.
21.	王昭昳, 張濤, 楊濱, 等. 基于徑向基函數神經網絡的腦損傷電阻抗成像仿真研究. 中國醫學裝備, 2023, 20(3): 1-5.
22.	田翔, 葉健安, 張靚靚, 等. PEUNet: 一種用于腦出血病灶檢測的多頻電阻抗成像方法. 空軍軍醫大學學報, 2023, https://link.cnki.net/urlid/61.1526.R.20230911.0934.002.
23.	Li F, Tan C, Dong F. Electrical resistance tomography image reconstruction with densely connected convolutional neural network. IEEE Trans Instrum Meas, 2021, 70: 4500811.
24.	Zhang X, Wang Z, Fu R, et al. V-shaped dense denoising convolutional neural network for electrical impedance tomography. IEEE Trans Instrum Meas, 2022, 71: 4503014.
25.	王子辰, 付榮, 張新宇, 等. 基于DeepCG的肺部電阻抗成像方法. 中國生物醫學工程學報, 2023, 42(2): 148-157.
26.	Ni A, Dong X, Yang G, et al. Image reconstruction incorporated with the skull inhomogeneity for electrical impedance tomography. Comput Med Imaging Graphics, 2008, 32(5): 409-415.
27.	祁乃興, 席宗翰, 劉秀芬. 腦梗塞腦出血灶形態分析. 蘇州醫學院學報, 1994, 14(6): 552.
28.	Hu D, Tian Y, Chang L, et al. Estimation of combustion temperature field from the electrical admittivity distribution obtained by electrical tomography. IEEE Trans Instrum Meas, 2020, 69(9): 6271-6280.
29.	Liu S, Huang Y, Wu H, et al. Efficient multitask structure-aware sparse Bayesian learning for frequency-difference electrical impedance tomography. IEEE Trans Ind Inf, 2021, 17(1): 463-472.
30.	Borsic A, Graham B M, Adler A, et al. In vivo impedance imaging with total variation regularization. IEEE Trans Med Imaging, 2010, 29(1): 44-54.
31.	Li Haoting, Chen Rongqing, Xu Canhua, et al. Unveiling the development of intracranial injury using dynamic brain EIT: an evaluation of current reconstruction algorithms. Physiol Meas, 2017, 38(9): 1176-1790.

1. 王隴德, 彭斌, 張鴻祺, 等. 《中國腦卒中防治報告2020》概要. 中國腦血管病雜志, 2022, 19(2): 136-144.
2. 張敏敏, 吳濤, 吳雄楓, 等. 神經內鏡手術對幕上高血壓性腦出血患者的療效分析: 一項單中心回顧性病例對照研究. 海軍軍醫大學學報, 2024, 45(4): 421-426.
3. Shi K B, Tian D C, Li Z G, et al. Global brain inflammation in stroke. Lancet Neurol, 2019, 18(11): 1058-1066.
4. Rindler R S, Allen J W, Barrow J W, et al. Neuroimaging of intracerebral hemorrhage. Neurosurgery, 2020, 86(5): E414-E423.
5. 楊琳, 代萌, 徐燦華, 等. 正常腦組織和腦卒中組織的電阻抗頻譜特性研究. 醫療衛生裝備, 2019, 40(2): 16-20,35.
6. 王緩, 鄭欣, 鐘鳴. 急性腦損傷并發ARDS患者中乳酸水平與EIT中背側通氣分布關聯性的臨床研究. 中國臨床新醫學, 2024, 17(2): 145-150.
7. 李穎. 基于組織系數的生物組織電特性分析方法研究. 醫療衛生裝備, 2022, 43(5): 8-13.
8. 白世展, 李文勝, 林海軍, 等. 基于徑向基函數神經網絡的肺部加權頻差電阻抗成像方法. 生物化學與生物物理進展, 2023, 50(7): 1755-1766.
9. 趙明康, 劉珺, 郭忠圣, 等. 結合生成對抗網絡的電阻抗斷層成像技術在肺通氣監測中的應用. 生物醫學工程學雜志, 2024, 41(1): 105-113.
10. 張濤, 安志偉, 劉學超, 等. 腦部電阻抗有限元模型的腦膜結構仿真. 醫療衛生裝備, 2020, 41(4): 35-38.
11. 劉凱, 李安琪, 李芳, 等. 基于三維電阻抗層析成像的乳腺癌傳感器的設計. 分析化學, 2024, 52(2): 248-258.
12. 徐德洪, 鄧琪, 姚佳烽. 基于電阻抗成像技術的細胞檢測傳感器開發. 傳感器與微系統, 2023, 42(11): 93-95, 104.
13. Wang H, Liu K, Wu Y, et al. Image reconstruction for electrical impedance tomography using radial basis function neural network based on hybrid particle swarm optimization algorithm. IEEE Sensors Journal, 2021, 21(2): 1926-1934.
14. Sun B, Yue S, Hao Z, et al. An improved tikhonov regularization method for lung cancer monitoring using electrical impedance tomography. IEEE Sensors Journal, 2019, 19(8): 3049-3057.
15. Lee K, Woo E J, Seo J K. A fidelity-embedded regularization method for robust electrical impedance tomography. IEEE Trans Med Imaging, 2018, 37(9): 1970-1977.
16. Liu X, Li H, Ma H, et al. An iterative damped least-squares algorithm for simultaneously monitoring the development of hemorrhagic and secondary ischemic lesions in brain injuries. Med Biol Eng Comput, 2019, 57(9): 1917-1931.
17. Shi Y, Wu Y, Wang M, et al. Sparse image reconstruction of intracerebral hemorrhage with electrical impedance tomography. J Med Imaging, 2021, 8(1): 014501.
18. Xiang J, Dong Y, Yang Y. FISTA-Net: learning a fast iterative shrinkage thresholding network for inverse problems in imaging. IEEE Trans Med Imaging, 2021, 40(5): 1329-1339.
19. Hamilton S J, Hanninen A, Hauptmann A, et al. Beltrami-net: domain-independent deep D-bar learning for absolute imaging with electrical impedance tomography (a-EIT). Physiol Meas, 2019, 40(7): 074002.
20. Li X, Zhang R, Wang Q, et al. SAR-CGAN: improved generative adversarial network for EIT reconstruction of lung diseases. Biomed Signal Process Control, 2023, 81: 104421.
21. 王昭昳, 張濤, 楊濱, 等. 基于徑向基函數神經網絡的腦損傷電阻抗成像仿真研究. 中國醫學裝備, 2023, 20(3): 1-5.
22. 田翔, 葉健安, 張靚靚, 等. PEUNet: 一種用于腦出血病灶檢測的多頻電阻抗成像方法. 空軍軍醫大學學報, 2023, https://link.cnki.net/urlid/61.1526.R.20230911.0934.002.
23. Li F, Tan C, Dong F. Electrical resistance tomography image reconstruction with densely connected convolutional neural network. IEEE Trans Instrum Meas, 2021, 70: 4500811.
24. Zhang X, Wang Z, Fu R, et al. V-shaped dense denoising convolutional neural network for electrical impedance tomography. IEEE Trans Instrum Meas, 2022, 71: 4503014.
25. 王子辰, 付榮, 張新宇, 等. 基于DeepCG的肺部電阻抗成像方法. 中國生物醫學工程學報, 2023, 42(2): 148-157.
26. Ni A, Dong X, Yang G, et al. Image reconstruction incorporated with the skull inhomogeneity for electrical impedance tomography. Comput Med Imaging Graphics, 2008, 32(5): 409-415.
27. 祁乃興, 席宗翰, 劉秀芬. 腦梗塞腦出血灶形態分析. 蘇州醫學院學報, 1994, 14(6): 552.
28. Hu D, Tian Y, Chang L, et al. Estimation of combustion temperature field from the electrical admittivity distribution obtained by electrical tomography. IEEE Trans Instrum Meas, 2020, 69(9): 6271-6280.
29. Liu S, Huang Y, Wu H, et al. Efficient multitask structure-aware sparse Bayesian learning for frequency-difference electrical impedance tomography. IEEE Trans Ind Inf, 2021, 17(1): 463-472.
30. Borsic A, Graham B M, Adler A, et al. In vivo impedance imaging with total variation regularization. IEEE Trans Med Imaging, 2010, 29(1): 44-54.
31. Li Haoting, Chen Rongqing, Xu Canhua, et al. Unveiling the development of intracranial injury using dynamic brain EIT: an evaluation of current reconstruction algorithms. Physiol Meas, 2017, 38(9): 1176-1790.

Journal of Biomedical Engineering

Image reconstruction for cerebral hemorrhage based on improved densely-connected fully convolutional neural network

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