3-D EIT Image Reconstruction Using a Block-Based Compressed Sensing Approach


Electrical impedance tomography (EIT) is a fast and cost-effective technique that provides a tomographic conductivity image of a subject from boundary current-voltage data. This paper proposes a time and memory efficient method for solving a large scale 3D EIT image reconstruction problem and the ill-posed linear inverse problem. First, we use block-based sampling for a large number of measured data from many electrodes. This method will reduce the size of Jacobian matrix and can improve accuracy of reconstruction by using more electrodes. And then, a sparse matrix reduction technique is proposed using thresholding to set very small values of the Jacobian matrix to zero. By adjusting the Jacobian matrix into a sparse format, the element with zeros would be eliminated, which results in a saving of memory requirement. Finally, we built up the relationship between compressed sensing and EIT definitely and induce the CS: two-step Iterative Shrinkage/Thresholding and block-based method into EIT image reconstruction algorithm. The results show that block-based compressed sensing enables the large scale 3D EIT problem to be efficient. For a 72-electrodes EIT system, our proposed method could save at least 61% of memory and reduce time by 72% than compressed sensing method only. The improvements will be obvious by using more electrodes. And this method is not only better at anti-noise, but also faster and better resolution.

Share and Cite:

Dung, L. , Yang, C. and Wu, Y. (2014) 3-D EIT Image Reconstruction Using a Block-Based Compressed Sensing Approach. Journal of Computer and Communications, 2, 34-40. doi: 10.4236/jcc.2014.213005.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Cheney, M., Isaacson, D. and Newell, J.C. (1999) Electrical Impedance Tomography. SIAM Review, 41, 85-101. http://dx.doi.org/10.1137/S0036144598333613
[2] Vauhkonen, P.J., Vauhkonen, M. and Savolai, T. (1999) Three Dimensional Electrical Impedance Tomography Based on the Complete Electrode Model. IEEE Transactions on Biomedical Engineering, 46, 1150-1160. http://dx.doi.org/10.1109/10.784147
[3] Baraniuk, R.G., Cevher, V., Duarte, M.F. and Hegde, C. (2010) Model-Based Compressive Sensing. IEEE Transactions on Information Theory, 56, 1982-2001. http://dx.doi.org/10.1109/TIT.2010.2040894
[4] Yang, C.L., Wei, H.Y., Adler, A. and Soleimani, M. (2013) Reducing Computational Costs in Large Scale 3D EIT by Using a Sparse Jacobian Matrix with Block-Wise CGLS Reconstruction. Physiological Measurement, 34, 645-658. http://dx.doi.org/10.1088/0967-3334/34/6/645
[5] Donoho, D.L. (2006) Compressed Sensing. IEEE Transactions on Information Theory, 52, 1289-1306. http://dx.doi.org/10.1109/TIT.2006.871582
[6] Bioucas-Dias, J. and Figueiredo, M. (2007) A New TwIST: Two-Step Iterative Shrinkage/Thresholding Algorithms for Image Restoration. IEEE Transactions on Image Processing, 16, 2992-3004. http://dx.doi.org/10.1109/TIP.2007.909319
[7] Tehrani, J.N., Jin, C., McEwan, A. and van Schaik, A. (2010) A Comparison between Compressed Sensing Algorithms in Electrical Impedance Tomography. IEEE Engineering in Medicine & Biology Society, 2010, 3109-3112.
[8] Adler, A. and Lionheart, W.R.B. (2006) Uses and Abuses of EIDORS: An Extensible Software Base for EIT. Physiological Measurement, 27, S25-S42. http://dx.doi.org/10.1088/0967-3334/27/5/S03
[9] Borsic, A., Graham, B.M., Adler, A. and Lionheart, W.R.B. (2010) In Vivo Impedance Imaging with Total Variation Regularization. IEEE Transactions on Medical Imaging, 29, 44-54. http://dx.doi.org/10.1109/TMI.2009.2022540
[10] Adler, A., Arnold, J.H., Bayford, R., Borsic, A., Brown, B., Dixon, P., Faes, T.J.C., Frerichs, I., Gagnon, H., G?rber, Y., Grychtol, B., Hahn, G., Lionheart, W.R.B., Malik, A., Patterson, R.P., Stocks, J., Tizzard, A., Weiler, N. and Wolf, G.K. (2009) GREIT: A Unified Approach to 2D Linear EIT Reconstruction of Lung Images. Physiological Measurement, 30, S35-S55. http://dx.doi.org/10.1088/0967-3334/30/6/S03

Copyright © 2023 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.