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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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.	张敏敏, 吴涛, 吴雄枫, 等. 神经内镜手术对幕上高血压性脑出血患者的疗效分析: 一项单中心回顾性病例对照研究. 海军军医大学学报, 2024, 45(4): 421-426.
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7.	李颖. 基于组织系数的生物组织电特性分析方法研究. 医疗卫生装备, 2022, 43(5): 8-13.
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9.	赵明康, 刘珺, 郭忠圣, 等. 结合生成对抗网络的电阻抗断层成像技术在肺通气监测中的应用. 生物医学工程学杂志, 2024, 41(1): 105-113.
10.	张涛, 安志伟, 刘学超, 等. 脑部电阻抗有限元模型的脑膜结构仿真. 医疗卫生装备, 2020, 41(4): 35-38.
11.	刘凯, 李安琪, 李芳, 等. 基于三维电阻抗层析成像的乳腺癌传感器的设计. 分析化学, 2024, 52(2): 248-258.
12.	徐德洪, 邓琪, 姚佳烽. 基于电阻抗成像技术的细胞检测传感器开发. 传感器与微系统, 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, 张涛, 杨滨, 等. 基于径向基函数神经网络的脑损伤电阻抗成像仿真研究. 中国医学装备, 2023, 20(3): 1-5.
22.	田翔, 叶健安, 张靓靓, 等. PEUNet: 一种用于脑出血病灶检测的多频电阻抗成像方法. 空军军医大学学报, 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, 付荣, 张新宇, 等. 基于DeepCG的肺部电阻抗成像方法. 中国生物医学工程学报, 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.	祁乃兴, 席宗翰, 刘秀芬. 脑梗塞脑出血灶形态分析. 苏州医学院学报, 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. 王陇德, 彭斌, 张鸿祺, 等. 《中国脑卒中防治报告2020》概要. 中国脑血管病杂志, 2022, 19(2): 136-144.
2. 张敏敏, 吴涛, 吴雄枫, 等. 神经内镜手术对幕上高血压性脑出血患者的疗效分析: 一项单中心回顾性病例对照研究. 海军军医大学学报, 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. 杨琳, 代萌, 徐灿华, 等. 正常脑组织和脑卒中组织的电阻抗频谱特性研究. 医疗卫生装备, 2019, 40(2): 16-20,35.
6. 王缓, 郑欣, 钟鸣. 急性脑损伤并发ARDS患者中乳酸水平与EIT中背侧通气分布关联性的临床研究. 中国临床新医学, 2024, 17(2): 145-150.
7. 李颖. 基于组织系数的生物组织电特性分析方法研究. 医疗卫生装备, 2022, 43(5): 8-13.
8. 白世展, 李文胜, 林海军, 等. 基于径向基函数神经网络的肺部加权频差电阻抗成像方法. 生物化学与生物物理进展, 2023, 50(7): 1755-1766.
9. 赵明康, 刘珺, 郭忠圣, 等. 结合生成对抗网络的电阻抗断层成像技术在肺通气监测中的应用. 生物医学工程学杂志, 2024, 41(1): 105-113.
10. 张涛, 安志伟, 刘学超, 等. 脑部电阻抗有限元模型的脑膜结构仿真. 医疗卫生装备, 2020, 41(4): 35-38.
11. 刘凯, 李安琪, 李芳, 等. 基于三维电阻抗层析成像的乳腺癌传感器的设计. 分析化学, 2024, 52(2): 248-258.
12. 徐德洪, 邓琪, 姚佳烽. 基于电阻抗成像技术的细胞检测传感器开发. 传感器与微系统, 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, 张涛, 杨滨, 等. 基于径向基函数神经网络的脑损伤电阻抗成像仿真研究. 中国医学装备, 2023, 20(3): 1-5.
22. 田翔, 叶健安, 张靓靓, 等. PEUNet: 一种用于脑出血病灶检测的多频电阻抗成像方法. 空军军医大学学报, 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, 付荣, 张新宇, 等. 基于DeepCG的肺部电阻抗成像方法. 中国生物医学工程学报, 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. 祁乃兴, 席宗翰, 刘秀芬. 脑梗塞脑出血灶形态分析. 苏州医学院学报, 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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