Research on electrical impedance tomography for pediatric pneumonia based on an improved radial basis function neural network_Journal of Biomedical Engineering

Authors：

WANG Ruiqi ¹ , YU Chengtao ¹ , WU Yang ² , YAO Jiafeng ³ , WANG Huaiyan ⁴ ,  DING Li ¹

1. School of Mechanical Engineering, Jiangsu University of Technology, Changzhou, Jiangsu 213001, P. R. China;
2. School of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, P. R. China;
3. School of Physics and Optoelectronic Engineering, Jinan University, Guangzhou 510632, P. R. China;
4. Department of Pediatrics, Changzhou Maternal and Child Health Hospital, Changzhou Medical Center of Nanjing Medical University, Changzhou, Jiangsu 213000, P. R. China;

Corresponding?author：

DING Li, Email: nuaadli@163.com

Keywords：

Pediatric pneumonia; Electrical impedance tomography; Crested porcupine optimization; Adaptive moment estimation; Radial basis function neural network

DOI：

10.7507/1001-5515.202512072

Video：

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In the diagnosis of pediatric pneumonia, the weak conductivity contrast of lung tissues leads to limited image resolution in electrical impedance tomography (EIT). To improve the quality of image reconstruction, this study proposes a radial basis function neural network optimized by the crested porcupine optimizer and adaptive moment estimation (CPOA-RBFNN). By integrating the global search capability of swarm intelligence with the adaptive characteristics of gradient based optimization, the proposed method enhances both imaging accuracy and robustness. A total of 23 000 simulated datasets of pediatric pneumonia were constructed for training and testing. The proposed method was compared with Tikhonov regularization and the conventional radial basis function neural network (RBFNN), and evaluated using root mean square error (RMSE), image correlation coefficient (ICC), structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR). Results show that under a noise level of 50 dB, the proposed method achieves the lowest RMSE (0.098), the highest ICC (0.922), and relatively high SSIM (0.909) and PSNR (9.295). Further clinical validation demonstrates that CPOA-RBFNN provides superior structural fidelity and lesion distinguishability in pneumonia reconstruction. In conclusion, the proposed method offers an effective solution for non-invasive, high-precision imaging and bedside-assisted diagnosis of pediatric pneumonia.

Citation： WANG Ruiqi, YU Chengtao, WU Yang, YAO Jiafeng, WANG Huaiyan, DING Li. Research on electrical impedance tomography for pediatric pneumonia based on an improved radial basis function neural network. Journal of Biomedical Engineering, 2026, 43(3): 589-597. doi: 10.7507/1001-5515.202512072 Copy

1.	Rueda Z V, Aguilar Y, Maya M A, et al. Etiology and the challenge of diagnostic testing of community-acquired pneumonia in children and adolescents. BMC Pediatr, 2022, 22(1): 169.
2.	Chen Y, Li L, Wang C, et al. Necrotizing pneumonia in children: early recognition and management. J Clin Med, 2023, 12(6): 2256.
3.	Passiglia F, Cinquini M, Bertolaccini L, et al. Benefits and harms of lung cancer screening by chest computed tomography: a systematic review and meta-analysis. J Clin Oncol, 2021, 39(23): 2574-2585.
4.	Bosch de Basea M, Thierry-Chef I, Harbron R, et al. Risk of hematological malignancies from CT radiation exposure in children, adolescents and young adults. Nat Med, 2023, 29(12): 3111-3119.
5.	Qu S, Feng E, Dong D, et al. Early screening of lung function by electrical impedance tomography in people with normal spirometry reveals unrecognized pathological features. Nat Commun, 2025, 16(1): 622.
6.	高國斌, 李坤, 李俊瑤, 等. 基于霜冰算法優化變分模態分解的電阻抗斷層掃描中肺通氣和肺灌注信號分離方法研究. 生物醫學工程學雜志, 2025, 42(2): 228-236.
7.	Scaramuzzo G, Pavlovsky B, Adler A, et al. Electrical impedance tomography monitoring in adult ICU patients: state-of-the-art, recommendations for standardized acquisition, processing, and clinical use, and future directions. Crit Care, 2024, 28(1): 377.
8.	Ako A A, Ismaiel A, Rastogi S. Electrical impedance tomography in neonates: a review. Pediatr Res, 2025, 98(2): 431-441.
9.	Trastoy-Quintela J, Ciutad-Celdran M, Hernandez-Platero L, et al. Electric impedance tomography to monitor body positioning and chest physiotherapy in mechanically ventilated pediatric intensive care unit patients. Sci Rep, 2025, 15(1): 36741.
10.	Moersdorf J, Muders T, Putensen C, et al. Monitoring of regional ventilation distribution using electrical impedance tomography in pediatric patients with chest physiotherapy-a feasibility study. Pediatr Pulmonol, 2025, 60(2): e71014.
11.	Song Y, Xiong L, Liu Z, et al. Research on human lung impedance tomography based on soft thresholding image segmentation and reduced-order Tikhonov regularization. Appl Sci, 2024, 14(8): 3309.
12.	Alghamdi A M A, Car?e M S, Everink J M, et al. Spatial regularization and level-set methods for experimental electrical impedance tomography with partial data. Applied Mathematics for Modern Challenges, 2024, 2(2): 165-186.
13.	Wang Z, Zhang T, Wang Q. Unrolling reweighted total variation-based split bregman iterative framework for electrical impedance tomography image reconstruction. IEEE Trans Comput Imaging, 2025, 11: 748-763.
14.	Dimas C, Alimisis V, Uzunoglu N, et al. Advances in electrical impedance tomography inverse problem solution methods: from traditional regularization to deep learning. IEEE Access, 2024, 12: 47797-47829.
15.	Homa L, Sannamani M, Thomas A J, et al. Enhanced damage imaging in three-dimensional composite structures via electrical impedance tomography with mixed and level set regularization. NDT E Int, 2023, 137: 102830.
16.	Zhang H, Cai R, Yang Y, et al. Unsupervised deep learning-based error image prior (DLEIP) algorithm for lung electrical impedance tomography (EIT). IEEE Internet Things J, 2025, 12(15): 31399-31407.
17.	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.
18.	Zhang H, Wang Q, Li N. DA-net: a dense attention reconstruction network for lung electrical impedance tomography (EIT). IEEE Internet Things J, 2024, 11(12): 22107-22115.
19.	Liu D, Wang J, Shan Q, et al. DeepEIT: deep image prior enabled electrical impedance tomography. IEEE Trans Pattern Anal Mach Intell, 2023, 45(8): 9627-9638.
20.	Carcavilla A C, Meribout M. Algorithms on electrical impedance tomography, focusing on deep learning architectures and their implementations: a review. IEEE Sens J, 2025, 25(18): 34252-34274.
21.	Dong Q, Zhang Y, He Q, et al. Image reconstruction method for electrical impedance tomography based on RBF and attention mechanism. Comput Electr Eng, 2023, 110: 108826.
22.	Nomvussi S A K, Mikulka J. Enhanced image reconstruction in electrical impedance tomography using radial basis function neural networks. Meas Sci Rev, 2024, 24(6): 200-210.
23.	Faiyaz C A, Shahrear P, Shamim R A, et al. Comparison of different radial basis function networks for the electrical impedance tomography (EIT) inverse problem. Algorithms, 2023, 16(10): 461.
24.	吳陽, 劉凱, 陳柏, 等. 自適應粒子群優化算法優化徑向基函數神經網絡用于電阻抗成像圖像重建. 儀器儀表學報, 2020, 41(6): 240-249.
25.	Hassan H, Tallman T N. Precise damage shaping in self-sensing composites using electrical impedance tomography and genetic algorithms. Struct Health Monit, 2023, 22(1): 372-387.
26.	Li J, Dong X, Ruan S, et al. A parallel integrated learning technique of improved particle swarm optimization and BP neural network and its application. Sci Rep, 2022, 12(1): 19325.
27.	Salinas-Gutiérrez R, Mu?oz Zavala A E. An explicit exploration strategy for evolutionary algorithms. Appl Soft Comput, 2023, 140: 110230.
28.	Abdel-Basset M, Mohamed R, Abouhawwash M. Crested porcupine optimizer: a new nature-inspired metaheuristic. Knowl Based Syst, 2024, 284: 111257.
29.	Adalja D, Patel P, Mashru N, et al. A new multi objective crested porcupines optimization algorithm for solving optimization problems. Sci Rep, 2025, 15(1): 14380.
30.	Yang J, Long Q. A modification of adaptive moment estimation (adam) for machine learning. J Ind Manag Optim, 2024, 20(7): 2516-2540.
31.	Abdulkadirov R, Lyakhov P, Nagornov N. Survey of optimization algorithms in modern neural networks. Mathematics, 2023, 11(11): 2466.
32.	Yang L, Gao Z, Wang C, et al. Evaluation of adjacent and opposite current injection patterns for a wearable chest electrical impedance tomography system. Physiol Meas, 2024, 45(2): 025004.
33.	Strauss T, Khan T. Implicit solutions of the electrical impedance tomography inverse problem in the continuous domain with deep neural networks. Entropy, 2023, 25(3): 493.
34.	Ma E. An equilibrium finite element method for electrical impedance tomography. Eng Res Express, 2023, 5(1): 015042.
35.	Tallman T N, Smyl D, Homa L, et al. The effect of different regularization approaches on damage imaging via electrical impedance tomography. J Nondestruct Eval, 2025, 44(2): 1-14.
36.	Tsoulos I G, Charilogis V, Tsalikakis D. From initialization to convergence: a three-stage technique for robust RBF network training. AI, 2025, 6(11): 280.
37.	Darma P N, Takei M. High-speed and accurate meat composition imaging by mechanically-flexible electrical impedance tomography with k-nearest neighbor and fuzzy k-means machine learning approaches. IEEE Access, 2021, 9: 38792-38801.
38.	Gabriel C, Gabriel S, Corthout E. The dielectric properties of biological tissues: I. Literature survey. Phys Med Biol, 1996, 41(11): 2231-2249.
39.	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 Adv, 2019, 9(12): 125310.
40.	Yu H, Liu H, Liu Z, et al. High-resolution conductivity reconstruction by electrical impedance tomography using structure-aware hybrid-fusion learning. Comput Methods Programs Biomed, 2024, 243: 107861.
41.	Zhu L, Lu W, Soleimani M, et al. Electrical impedance tomography guided by digital twins and deep learning for lung monitoring. IEEE Trans Instrum Meas, 2023, 72: 1-9.
42.	Zitzmann A, Strübing A, Reuter D A, et al. Enhancing tidal volume estimation from electrical impedance tomography (EIT) by applying human anthropometric information. J Clin Monit Comput, 2026, 40(1): 93-101.

1. Rueda Z V, Aguilar Y, Maya M A, et al. Etiology and the challenge of diagnostic testing of community-acquired pneumonia in children and adolescents. BMC Pediatr, 2022, 22(1): 169.
2. Chen Y, Li L, Wang C, et al. Necrotizing pneumonia in children: early recognition and management. J Clin Med, 2023, 12(6): 2256.
3. Passiglia F, Cinquini M, Bertolaccini L, et al. Benefits and harms of lung cancer screening by chest computed tomography: a systematic review and meta-analysis. J Clin Oncol, 2021, 39(23): 2574-2585.
4. Bosch de Basea M, Thierry-Chef I, Harbron R, et al. Risk of hematological malignancies from CT radiation exposure in children, adolescents and young adults. Nat Med, 2023, 29(12): 3111-3119.
5. Qu S, Feng E, Dong D, et al. Early screening of lung function by electrical impedance tomography in people with normal spirometry reveals unrecognized pathological features. Nat Commun, 2025, 16(1): 622.
6. 高國斌, 李坤, 李俊瑤, 等. 基于霜冰算法優化變分模態分解的電阻抗斷層掃描中肺通氣和肺灌注信號分離方法研究. 生物醫學工程學雜志, 2025, 42(2): 228-236.
7. Scaramuzzo G, Pavlovsky B, Adler A, et al. Electrical impedance tomography monitoring in adult ICU patients: state-of-the-art, recommendations for standardized acquisition, processing, and clinical use, and future directions. Crit Care, 2024, 28(1): 377.
8. Ako A A, Ismaiel A, Rastogi S. Electrical impedance tomography in neonates: a review. Pediatr Res, 2025, 98(2): 431-441.
9. Trastoy-Quintela J, Ciutad-Celdran M, Hernandez-Platero L, et al. Electric impedance tomography to monitor body positioning and chest physiotherapy in mechanically ventilated pediatric intensive care unit patients. Sci Rep, 2025, 15(1): 36741.
10. Moersdorf J, Muders T, Putensen C, et al. Monitoring of regional ventilation distribution using electrical impedance tomography in pediatric patients with chest physiotherapy-a feasibility study. Pediatr Pulmonol, 2025, 60(2): e71014.
11. Song Y, Xiong L, Liu Z, et al. Research on human lung impedance tomography based on soft thresholding image segmentation and reduced-order Tikhonov regularization. Appl Sci, 2024, 14(8): 3309.
12. Alghamdi A M A, Car?e M S, Everink J M, et al. Spatial regularization and level-set methods for experimental electrical impedance tomography with partial data. Applied Mathematics for Modern Challenges, 2024, 2(2): 165-186.
13. Wang Z, Zhang T, Wang Q. Unrolling reweighted total variation-based split bregman iterative framework for electrical impedance tomography image reconstruction. IEEE Trans Comput Imaging, 2025, 11: 748-763.
14. Dimas C, Alimisis V, Uzunoglu N, et al. Advances in electrical impedance tomography inverse problem solution methods: from traditional regularization to deep learning. IEEE Access, 2024, 12: 47797-47829.
15. Homa L, Sannamani M, Thomas A J, et al. Enhanced damage imaging in three-dimensional composite structures via electrical impedance tomography with mixed and level set regularization. NDT E Int, 2023, 137: 102830.
16. Zhang H, Cai R, Yang Y, et al. Unsupervised deep learning-based error image prior (DLEIP) algorithm for lung electrical impedance tomography (EIT). IEEE Internet Things J, 2025, 12(15): 31399-31407.
17. 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.
18. Zhang H, Wang Q, Li N. DA-net: a dense attention reconstruction network for lung electrical impedance tomography (EIT). IEEE Internet Things J, 2024, 11(12): 22107-22115.
19. Liu D, Wang J, Shan Q, et al. DeepEIT: deep image prior enabled electrical impedance tomography. IEEE Trans Pattern Anal Mach Intell, 2023, 45(8): 9627-9638.
20. Carcavilla A C, Meribout M. Algorithms on electrical impedance tomography, focusing on deep learning architectures and their implementations: a review. IEEE Sens J, 2025, 25(18): 34252-34274.
21. Dong Q, Zhang Y, He Q, et al. Image reconstruction method for electrical impedance tomography based on RBF and attention mechanism. Comput Electr Eng, 2023, 110: 108826.
22. Nomvussi S A K, Mikulka J. Enhanced image reconstruction in electrical impedance tomography using radial basis function neural networks. Meas Sci Rev, 2024, 24(6): 200-210.
23. Faiyaz C A, Shahrear P, Shamim R A, et al. Comparison of different radial basis function networks for the electrical impedance tomography (EIT) inverse problem. Algorithms, 2023, 16(10): 461.
24. 吳陽, 劉凱, 陳柏, 等. 自適應粒子群優化算法優化徑向基函數神經網絡用于電阻抗成像圖像重建. 儀器儀表學報, 2020, 41(6): 240-249.
25. Hassan H, Tallman T N. Precise damage shaping in self-sensing composites using electrical impedance tomography and genetic algorithms. Struct Health Monit, 2023, 22(1): 372-387.
26. Li J, Dong X, Ruan S, et al. A parallel integrated learning technique of improved particle swarm optimization and BP neural network and its application. Sci Rep, 2022, 12(1): 19325.
27. Salinas-Gutiérrez R, Mu?oz Zavala A E. An explicit exploration strategy for evolutionary algorithms. Appl Soft Comput, 2023, 140: 110230.
28. Abdel-Basset M, Mohamed R, Abouhawwash M. Crested porcupine optimizer: a new nature-inspired metaheuristic. Knowl Based Syst, 2024, 284: 111257.
29. Adalja D, Patel P, Mashru N, et al. A new multi objective crested porcupines optimization algorithm for solving optimization problems. Sci Rep, 2025, 15(1): 14380.
30. Yang J, Long Q. A modification of adaptive moment estimation (adam) for machine learning. J Ind Manag Optim, 2024, 20(7): 2516-2540.
31. Abdulkadirov R, Lyakhov P, Nagornov N. Survey of optimization algorithms in modern neural networks. Mathematics, 2023, 11(11): 2466.
32. Yang L, Gao Z, Wang C, et al. Evaluation of adjacent and opposite current injection patterns for a wearable chest electrical impedance tomography system. Physiol Meas, 2024, 45(2): 025004.
33. Strauss T, Khan T. Implicit solutions of the electrical impedance tomography inverse problem in the continuous domain with deep neural networks. Entropy, 2023, 25(3): 493.
34. Ma E. An equilibrium finite element method for electrical impedance tomography. Eng Res Express, 2023, 5(1): 015042.
35. Tallman T N, Smyl D, Homa L, et al. The effect of different regularization approaches on damage imaging via electrical impedance tomography. J Nondestruct Eval, 2025, 44(2): 1-14.
36. Tsoulos I G, Charilogis V, Tsalikakis D. From initialization to convergence: a three-stage technique for robust RBF network training. AI, 2025, 6(11): 280.
37. Darma P N, Takei M. High-speed and accurate meat composition imaging by mechanically-flexible electrical impedance tomography with k-nearest neighbor and fuzzy k-means machine learning approaches. IEEE Access, 2021, 9: 38792-38801.
38. Gabriel C, Gabriel S, Corthout E. The dielectric properties of biological tissues: I. Literature survey. Phys Med Biol, 1996, 41(11): 2231-2249.
39. 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 Adv, 2019, 9(12): 125310.
40. Yu H, Liu H, Liu Z, et al. High-resolution conductivity reconstruction by electrical impedance tomography using structure-aware hybrid-fusion learning. Comput Methods Programs Biomed, 2024, 243: 107861.
41. Zhu L, Lu W, Soleimani M, et al. Electrical impedance tomography guided by digital twins and deep learning for lung monitoring. IEEE Trans Instrum Meas, 2023, 72: 1-9.
42. Zitzmann A, Strübing A, Reuter D A, et al. Enhancing tidal volume estimation from electrical impedance tomography (EIT) by applying human anthropometric information. J Clin Monit Comput, 2026, 40(1): 93-101.

Journal of Biomedical Engineering

Research on electrical impedance tomography for pediatric pneumonia based on an improved radial basis function neural network

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