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Quantitative Assessment of Image Reconstruction Algorithms in Electrical Impedance Tomography

Yıl 2025, Cilt: 8 Sayı: 1, 38 - 51, 31.05.2025
https://doi.org/10.34088/kojose.1556617

Öz

Electrical Impedance Tomography (EIT) is a noninvasive imaging technique used to estimate the internal conductivity distribution of a region that is either unknown or inaccessible. This is achieved by applying electrical currents to the region and measuring the resulting boundary voltages. The forward problem in EIT is typically solved using the Finite Element Method (FEM), and regularization techniques are employed to stabilize the ill-posed inverse problem during image reconstruction. This study evaluated the performance of two widely used image reconstruction algorithms: the delta conductivity method and the Jacobian (JAC)-based method. Both algorithms were tested on seven phantom images with varying levels of complexity to assess their effectiveness in different scenarios. The average Peak Signal-to-Noise Ratio (PSNR) and Mean Structural Similarity Index (MSSIM) were 35.71 dB and 0.93, respectively, indicating high reconstruction quality. However, the complexity of the images, such as intricate textures or multiple inclusions, resulted in reduced reconstruction accuracy. Although, both the delta conductivity and JAC methods proved effective in EIT image reconstruction, the JAC method shows superior performance in more challenging cases.

Kaynakça

  • [1] Li, Y., Wang, N., Fan, L.F., Zhao, P.F., Li, J. H., Huang, L., Wang, Z.Y., 2023. Robust electrical impedance tomography for biological application: a mini review. Heliyon, 9(4).
  • [2] Aller, M., Mera, D., Cotos, J. M., & Villaroya, S., 2023. Study and comparison of different Machine Learning-based approaches to solve the inverse problem in Electrical Impedance Tomographies. Neural Computing and Applications, 35(7), 5465-5477.
  • [3] Manisalı, H., 2020. Elektrik Empedans Tomografide Eliptik Yapılardaki İletkenlik Dağılımları Geriçatım Probleminin Yapay Sinir Ağları ile İncelenmesi. Yüksek Lisans Tezi, Hacettepe Üniversitesi.
  • [4] Oz, I., 2024. New Approaches in Engineering Sciences, Finite Element-Based Imaging for Electrical Impedance Tomography. Platanus Publishing, Turkey
  • [5] Ziegler, S., Santos, T., & Mueller, J. L. 2024. Regularized full waveform inversion for low frequency ultrasound tomography with a structural similarity EIT prior. Inverse Problems and Imaging. 18(1), 86-103.
  • [6] Ozkan, I., 1995. Elektriksel empedans tomografisi. Doktora tezi, Fen Bilimleri Enstitüsü Osmangazi Üniversitesi.
  • [7] Oz, I., 1996. Empedans tomografisinde sonlu elemanlar yönte-mi ile görüntü oluşturulması. Yüksek Lisans Tezi, Fen Bilimleri Enstitüsü, Dumlupınar Üniversitesi.
  • [8] Dimas, C., & Sotiriadis, P. P., 2018. Electrical impedance tomography image reconstruction for adjacent and opposite strategy using FEMM and EIDORS simulation models. IEEE 7th International Conference on Modern Circuits and Systems Technologies (MOCAST) (pp. 1-4).
  • [9] Hamilton, S. J., Lionheart, W. R. B., & Adler, A., 2019. Comparing D-bar and common regularization-based methods for electrical impedance tomography. Physiological measurement, 40(4), 044004.
  • [10] Cen, S., Jin, B., Shin, K., & Zhou, Z., 2023. Electrical impedance tomography with deep Calderón method. Journal of Computational Physics, 493, 112427.
  • [11] Brinckerhoff, M., 2018. Comparison of electrical impedance tomography reconstruction algorithms with EIDORS reconstruction software. Master's thesis, Clemson University.
  • [12] Mosquera, V., Gonzalez, C., & Ortega, E., 2019. Eidors-matlab interface for forward problem solving of electrical impedance tomography.
  • [13] Hauptmann, A., Kolehmainen, V., Mach, N. M., Savolainen, T., Seppänen, A., & Siltanen, S., 2017. Open 2D electrical impedance tomography data archive. arXiv preprint arXiv:1704.01178.
  • [14] Hakula, H., Hyvönen, N., & Tuominen, T., 2012. On the hp-adaptive solution of complete electrode model forward problems of electrical impedance tomography. Journal of computational and applied mathematics, 236(18), 4645-4659.
  • [15] Sarode, V., Chimurkar, P. M., & Cheeran, A. N., 2012. Electrical impedance tomography using EIDORS in a closed phantom. International Journal of Computer Applications, 48(19), 975-888.
  • [16] Zong, Z., Wang, Y., He, S., Zhu, Y. J., & Wei, Z., 2023. A compressive learning-based scheme for nonlinear reconstructions in electrical impedance tomography. IEEE Transactions on Instrumentation and Measurement.
  • [17] Sarode, V., Patkar, S., & Cheeran, A. N., 2013. Comparison of 2-D algorithms in ElT based image reconstruction. International Journal of Computer Applications, 69(8), 6-11.
  • [18] Yang, Y., Wu, H., & Jia, J. 2017. Image reconstruction for electrical impedance tomography using enhanced adaptive group sparsity with total variation. IEEE Sensors Journal, 17(17), 5589-5598.
  • [19] Pennati, F., Angelucci, A., Morelli, L., Bardini, S., Barzanti, E., Cavallini, F., & Aliverti, A., 2023. Electrical impedance tomography: From the traditional design to the novel frontier of wearables. Sensors, 23(3), 1182.
  • [20] Wang, Z., Zhang, X., Fu, R., Wang, D., Chen, X., & Wang, H., 2023. Electrical impedance tomography image reconstruction with attention-based deep convolutional neural network. IEEE Transactions on Instrumentation and Measurement, 72, 1-18.
  • [21] Salkım, E., & Abut, T., 2024. Human Head Transcranial Magnetic Stimulation Using Finite Element Method. Kocaeli Journal of Science and Engineering, 7(1), 62-70.
  • [22] Kilic, B., 2019. Impedance image reconstruction with artificial neural network in electrical impedance tomography. European Journal of Technique (EJT), 9(2), 137-144.
  • [23] Dong, Q., Zhang, Y., He, Q., Xu, C., & Pan, X., 2023. Image reconstruction method for electrical impedance tomography based on RBF and attention mechanism. Computers and Electrical Engineering., 110, 108826.
  • [24] Oz, I., 2024. Comparative Analysis of Wavelet Families in Image Compression, Featuring the Proposed New Wavelet. Turkish Journal of Science and Technology, 19(1), 279-294. https://doi.org/10.55525/tjst.1428424
  • [25] Oz, I. 2006. İki Boyutlu Ayrık Dalgacık Dönüşüm Filtreleri Kullanarak Sabit ve Hareketli Görüntü Sıkıştırma. Doktora Tezi, Sakarya Üniversitesi, Sakarya, Turkey.
  • [26] Oz, I, Oz, C, Yumusak N., 2001. Image compression using 2-D multiple-level discrete wavelet transform (DWT). Eleco 2001 International Conference on Electrical and Electronics Engineering, Turkey.
  • [27] Burukanli, M., & Yumuşak, N., 2024. TfrAdmCov: a robust transformer encoder based model with Adam optimizer algorithm for COVID-19 mutation prediction. Connection Science, 36(1), 2365334.
  • [28] Yılmaz, G. N., & Akar, G. B., 2022. SSIM Modelin Geliştirilmesine Dayanan Bir 3B Video Kalite Değerlendirme Metriği. Türkiye Bilişim Vakfı Bilgisayar Bilimleri ve Mühendisliği Dergisi, 15(1), 1-5.
  • [29] Sun, J., Zhu, Q., Fang, H., Wang, J., Zhou, W., Liu, Z., & Yang, Y., 2024. Multi-Modal EIT Image Reconstruction Using Deep Similarity Prior. IEEE International Instrumentation and Measurement Technology Conference (I2MTC) (pp. 1-6). IEEE.
  • [30] Chen, J., Wang, S., Wang, K., Abiri, P., Huang, Z. Y., Yin, J., ... & Hsiai, T. K., 2024. Machine learning‐directed electrical impedance tomography to predict metabolically vulnerable plaques. Bioengineering & translational medicine, 9(1), e10616.
  • [31] Yang, L., Li, Z., Dai, M., Fu, F., Möller, K., Gao, Y., & Zhao, Z., 2023. Optimal machine learning methods for prediction of high-flow nasal cannula outcomes using image features from electrical impedance tomography. Computer Methods and Programs in Biomedicine, 238, 107613.
  • [32] Salkım, E., 2023. Transcutaneous Nerve Stimulation Current Thresholds Based on Nerve Bending Angle and Nerve Termination Point. Kocaeli Journal of Science and Engineering, 6(2), 162-171.
  • [33] Liu, Benyuan, et al. "pyEIT: A python based framework for Electrical Impedance Tomography." SoftwareX 7 (2018), 304-308.
  • [34] Melek, N., 2024. The Importance of Rhythm Activity in Epilepsy EEG Signal Classification (An Educational Article). Sakarya University Journal of Computer and Information Sciences, 7(2), 138-155.
  • [35] Oz, I., 2024. Compression Methods For Satellite Images Using Wavelet Transform And Performance Evaluation. International Journal of Innovative Engineering Applications, 8(2), 72-81. doi:10.46460/ijiea.1440970

Quantitative Assessment of Image Reconstruction Algorithms in Electrical Impedance Tomography

Yıl 2025, Cilt: 8 Sayı: 1, 38 - 51, 31.05.2025
https://doi.org/10.34088/kojose.1556617

Öz

Electrical Impedance Tomography (EIT) is a noninvasive imaging technique used to estimate the internal conductivity distribution of a region that is either unknown or inaccessible. This is achieved by applying electrical currents to the region and measuring the resulting boundary voltages. The forward problem in EIT is typically solved using the Finite Element Method (FEM), and regularization techniques are employed to stabilize the ill-posed inverse problem during image reconstruction. This study evaluated the performance of two widely used image reconstruction algorithms: the delta conductivity method and the Jacobian (JAC)-based method. Both algorithms were tested on seven phantom images with varying levels of complexity to assess their effectiveness in different scenarios. The average Peak Signal-to-Noise Ratio (PSNR) and Mean Structural Similarity Index (MSSIM) were 35.71 dB and 0.93, respectively, indicating high reconstruction quality. However, the complexity of the images, such as intricate textures or multiple inclusions, resulted in reduced reconstruction accuracy. Although, both the delta conductivity and JAC methods proved effective in EIT image reconstruction, the JAC method shows superior performance in more challenging cases.

Kaynakça

  • [1] Li, Y., Wang, N., Fan, L.F., Zhao, P.F., Li, J. H., Huang, L., Wang, Z.Y., 2023. Robust electrical impedance tomography for biological application: a mini review. Heliyon, 9(4).
  • [2] Aller, M., Mera, D., Cotos, J. M., & Villaroya, S., 2023. Study and comparison of different Machine Learning-based approaches to solve the inverse problem in Electrical Impedance Tomographies. Neural Computing and Applications, 35(7), 5465-5477.
  • [3] Manisalı, H., 2020. Elektrik Empedans Tomografide Eliptik Yapılardaki İletkenlik Dağılımları Geriçatım Probleminin Yapay Sinir Ağları ile İncelenmesi. Yüksek Lisans Tezi, Hacettepe Üniversitesi.
  • [4] Oz, I., 2024. New Approaches in Engineering Sciences, Finite Element-Based Imaging for Electrical Impedance Tomography. Platanus Publishing, Turkey
  • [5] Ziegler, S., Santos, T., & Mueller, J. L. 2024. Regularized full waveform inversion for low frequency ultrasound tomography with a structural similarity EIT prior. Inverse Problems and Imaging. 18(1), 86-103.
  • [6] Ozkan, I., 1995. Elektriksel empedans tomografisi. Doktora tezi, Fen Bilimleri Enstitüsü Osmangazi Üniversitesi.
  • [7] Oz, I., 1996. Empedans tomografisinde sonlu elemanlar yönte-mi ile görüntü oluşturulması. Yüksek Lisans Tezi, Fen Bilimleri Enstitüsü, Dumlupınar Üniversitesi.
  • [8] Dimas, C., & Sotiriadis, P. P., 2018. Electrical impedance tomography image reconstruction for adjacent and opposite strategy using FEMM and EIDORS simulation models. IEEE 7th International Conference on Modern Circuits and Systems Technologies (MOCAST) (pp. 1-4).
  • [9] Hamilton, S. J., Lionheart, W. R. B., & Adler, A., 2019. Comparing D-bar and common regularization-based methods for electrical impedance tomography. Physiological measurement, 40(4), 044004.
  • [10] Cen, S., Jin, B., Shin, K., & Zhou, Z., 2023. Electrical impedance tomography with deep Calderón method. Journal of Computational Physics, 493, 112427.
  • [11] Brinckerhoff, M., 2018. Comparison of electrical impedance tomography reconstruction algorithms with EIDORS reconstruction software. Master's thesis, Clemson University.
  • [12] Mosquera, V., Gonzalez, C., & Ortega, E., 2019. Eidors-matlab interface for forward problem solving of electrical impedance tomography.
  • [13] Hauptmann, A., Kolehmainen, V., Mach, N. M., Savolainen, T., Seppänen, A., & Siltanen, S., 2017. Open 2D electrical impedance tomography data archive. arXiv preprint arXiv:1704.01178.
  • [14] Hakula, H., Hyvönen, N., & Tuominen, T., 2012. On the hp-adaptive solution of complete electrode model forward problems of electrical impedance tomography. Journal of computational and applied mathematics, 236(18), 4645-4659.
  • [15] Sarode, V., Chimurkar, P. M., & Cheeran, A. N., 2012. Electrical impedance tomography using EIDORS in a closed phantom. International Journal of Computer Applications, 48(19), 975-888.
  • [16] Zong, Z., Wang, Y., He, S., Zhu, Y. J., & Wei, Z., 2023. A compressive learning-based scheme for nonlinear reconstructions in electrical impedance tomography. IEEE Transactions on Instrumentation and Measurement.
  • [17] Sarode, V., Patkar, S., & Cheeran, A. N., 2013. Comparison of 2-D algorithms in ElT based image reconstruction. International Journal of Computer Applications, 69(8), 6-11.
  • [18] Yang, Y., Wu, H., & Jia, J. 2017. Image reconstruction for electrical impedance tomography using enhanced adaptive group sparsity with total variation. IEEE Sensors Journal, 17(17), 5589-5598.
  • [19] Pennati, F., Angelucci, A., Morelli, L., Bardini, S., Barzanti, E., Cavallini, F., & Aliverti, A., 2023. Electrical impedance tomography: From the traditional design to the novel frontier of wearables. Sensors, 23(3), 1182.
  • [20] Wang, Z., Zhang, X., Fu, R., Wang, D., Chen, X., & Wang, H., 2023. Electrical impedance tomography image reconstruction with attention-based deep convolutional neural network. IEEE Transactions on Instrumentation and Measurement, 72, 1-18.
  • [21] Salkım, E., & Abut, T., 2024. Human Head Transcranial Magnetic Stimulation Using Finite Element Method. Kocaeli Journal of Science and Engineering, 7(1), 62-70.
  • [22] Kilic, B., 2019. Impedance image reconstruction with artificial neural network in electrical impedance tomography. European Journal of Technique (EJT), 9(2), 137-144.
  • [23] Dong, Q., Zhang, Y., He, Q., Xu, C., & Pan, X., 2023. Image reconstruction method for electrical impedance tomography based on RBF and attention mechanism. Computers and Electrical Engineering., 110, 108826.
  • [24] Oz, I., 2024. Comparative Analysis of Wavelet Families in Image Compression, Featuring the Proposed New Wavelet. Turkish Journal of Science and Technology, 19(1), 279-294. https://doi.org/10.55525/tjst.1428424
  • [25] Oz, I. 2006. İki Boyutlu Ayrık Dalgacık Dönüşüm Filtreleri Kullanarak Sabit ve Hareketli Görüntü Sıkıştırma. Doktora Tezi, Sakarya Üniversitesi, Sakarya, Turkey.
  • [26] Oz, I, Oz, C, Yumusak N., 2001. Image compression using 2-D multiple-level discrete wavelet transform (DWT). Eleco 2001 International Conference on Electrical and Electronics Engineering, Turkey.
  • [27] Burukanli, M., & Yumuşak, N., 2024. TfrAdmCov: a robust transformer encoder based model with Adam optimizer algorithm for COVID-19 mutation prediction. Connection Science, 36(1), 2365334.
  • [28] Yılmaz, G. N., & Akar, G. B., 2022. SSIM Modelin Geliştirilmesine Dayanan Bir 3B Video Kalite Değerlendirme Metriği. Türkiye Bilişim Vakfı Bilgisayar Bilimleri ve Mühendisliği Dergisi, 15(1), 1-5.
  • [29] Sun, J., Zhu, Q., Fang, H., Wang, J., Zhou, W., Liu, Z., & Yang, Y., 2024. Multi-Modal EIT Image Reconstruction Using Deep Similarity Prior. IEEE International Instrumentation and Measurement Technology Conference (I2MTC) (pp. 1-6). IEEE.
  • [30] Chen, J., Wang, S., Wang, K., Abiri, P., Huang, Z. Y., Yin, J., ... & Hsiai, T. K., 2024. Machine learning‐directed electrical impedance tomography to predict metabolically vulnerable plaques. Bioengineering & translational medicine, 9(1), e10616.
  • [31] Yang, L., Li, Z., Dai, M., Fu, F., Möller, K., Gao, Y., & Zhao, Z., 2023. Optimal machine learning methods for prediction of high-flow nasal cannula outcomes using image features from electrical impedance tomography. Computer Methods and Programs in Biomedicine, 238, 107613.
  • [32] Salkım, E., 2023. Transcutaneous Nerve Stimulation Current Thresholds Based on Nerve Bending Angle and Nerve Termination Point. Kocaeli Journal of Science and Engineering, 6(2), 162-171.
  • [33] Liu, Benyuan, et al. "pyEIT: A python based framework for Electrical Impedance Tomography." SoftwareX 7 (2018), 304-308.
  • [34] Melek, N., 2024. The Importance of Rhythm Activity in Epilepsy EEG Signal Classification (An Educational Article). Sakarya University Journal of Computer and Information Sciences, 7(2), 138-155.
  • [35] Oz, I., 2024. Compression Methods For Satellite Images Using Wavelet Transform And Performance Evaluation. International Journal of Innovative Engineering Applications, 8(2), 72-81. doi:10.46460/ijiea.1440970
Toplam 35 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Biyomedikal Mühendisliği (Diğer)
Bölüm Makaleler
Yazarlar

İbrahim Öz 0000-0003-4593-917X

Yayımlanma Tarihi 31 Mayıs 2025
Gönderilme Tarihi 26 Eylül 2024
Kabul Tarihi 21 Aralık 2024
Yayımlandığı Sayı Yıl 2025 Cilt: 8 Sayı: 1

Kaynak Göster

APA Öz, İ. (2025). Quantitative Assessment of Image Reconstruction Algorithms in Electrical Impedance Tomography. Kocaeli Journal of Science and Engineering, 8(1), 38-51. https://doi.org/10.34088/kojose.1556617