Document Type : Original Article

Authors

1 Ph. D. University of Tehran-Faculty of Agriculture-Department of Agricultural Machinery Engineering

2 Associate Professor, University of Tehran-Faculty of Agriculture-Department of Agricultural Machinery Engineering

3 Professor, University of Tehran-Faculty of Agriculture-Department of Agricultural Machinery Engineering

10.22092/amsr.2024.364897.1476

Abstract

Magnetic induction tomography (MIT) is one of the new imaging techniques, and due to its characteristic such as non-intrusive and non-destructive, it has potential for use in many industries, including biological industries, multiphase flows, medical imaging, agriculture and food industries. Main parts of the MIT system are transmitter and receiver sensors, data acquisition system and image reconstruction algorithm. In this research, performance comparison of four image reconstruction algorithms in applied current magnetic induction tomography (AC-MIT) system was investigated. This system has two innovative annular electrodes as transmitter sensors and 648 coils as a receiver sensor. In order to evaluate the system performance, 12 combinations of target objects were used and image reconstruction was performed using linear back projection algorithm, Landweber iterative algorithm, Tikhonov regularization method and iterative Gauss-Newton algorithm. Size error (SE) and Relative image error (IE) parameters were used to evaluate the quality of the reconstructed images. The results showed that in all combinations of target objects, the IE values of iterative Gauss-Newton algorithm are lower than other algorithms. The results of size error parameter showed that in all four image reconstruction algorithms; increasing number of target objects increases SE parameter. As a general conclusion, it can be stated that iterative Gauss-Newton algorithm has a better performance compared to other algorithms.

Keywords

Babu, K. S., & Amamcharla, J. K. (2021). Rehydration characteristics of milk protein concentrate powders monitored by electrical resistance tomography. JDS Communications, 2(6), 313-318. https://doi.org/10.3168/jdsc.2021-0125.
Graham, B. M. (2007). Enhancements in electrical impedance tomography (EIT) image reconstruction for 3D lung imaging (Ph. D. Thesis), University of Ottawa, Ottawa, Canada.
Han, M., Cheng, X., & Xue, Y. (2016). Comparison with reconstruction algorithms in magnetic induction tomography. Physiological Measurement, 37(5), 683-697. https://doi.org/10.1088/0967-3334/37/5/683
Hao, L., Li, G., & Lin, L. (2013). Optimization of measurement arrangements for magnetic detection electrical impedance tomography. IEEE Transactions on Biomedical Engineering, 61(2), 444-452. https://doi.org/10.1109/TBME.2013.2280632.
Humplik, P., Cermak, P., & Zid, T. (2016). Electrical impedance tomography for decay diagnostics of Norway spruce (Picea abies): possibilities and opportunities. Silva Fennica, 50(1), 1341. https://doi.org/10.14214/sf.1341.
Jinchuang, Z., Wenli, F., Taoshen, L., & Shi, W. (2002). An image reconstruction algorithm based on a revised regularization method for electrical capacitance tomography. Measurement Science and Technology, 13(4), 638-640. http://dx.doi.org/10.1088/0957-0233/13/4/329.
Li, G., Hao, L., Chen, R., & Lin, L. (2012). A new electrode mode for magnetic detection electrical impeda 0nce tomography: Computer simulation study. IEEE Transactions on Magnetics, 48(10), 2543-2550. https://doi.org/10.1109/TMAG.2012.2200692.
Liu, X., & Wang, Y. (2022). An improved conjugate gradient image reconstruction algorithm for electromagnetic tomography. Sensing and Imaging, 23(1), https://doi.org/10.1007/s11220-021-00374-y.
Liu, X., Liu, Z., & Yue, Y. (2019). Simulation research of impact of number of coils in EMT sensors on reconstructed images quality. Sensing and Imaging, 20, 1-13. https://doi.org/10.1007/s11220-019-0250-2.
Liu, Z., Yang, G., He, N., & Tan, X. (2012). Landweber iterative algorithm based on regularization in electromagnetic tomography for multiphase flow measurement. Flow Measurement and Instrumentation, 27, 53-58. https://doi.org/10.1016/j.flowmeasinst.2012.04.011.
Ma, L., & Soleimani, M. (2018). Magnetic induction spectroscopy for permeability imaging. Scientific Reports, 8(1), 7025. https://doi.org/10.1038/s41598-018-25507-4.
Ma, L., McCann, D., & Hunt, A. (2017). Combining magnetic induction tomography and electromagnetic velocity tomography for water continuous multiphase flows. IEEE Sensors Journal, 17(24), 8271-8281. https://doi.org/10.1109/JSEN.2017.2758601.
Marefatallah, M., Breakey, D., & Sanders, R. S. (2021). Experimental study of local solid volume fraction fluctuations in a liquid fluidized bed: Particles with a wide range of stokes numbers. International Journal of Multiphase Flow, 135, 103348. https://doi.org/10.1016/j.ijmultiphaseflow.2020.103348.
Mary, B., Peruzzo, L., Boaga, J., Cenni, N., Schmutz, M., Wu, Y., & Cassiani, G. (2020). Time-lapse monitoring of root water uptake using electrical resistivity tomography and mise-à-la-masse: a vineyard infiltration experiment. Soil, 6(1), 95-114. https://doi.org/10.5194/soil-6-95-2020.
Olmos, A. M., Botella, G., Castillo, E., Morales, D. P., Banqueri, J., & García, A. (2012). A reconstruction method for electrical capacitance tomography based on image fusion techniques. Digital Signal Processing, 22(6), 885-893. https://doi.org/10.1016/j.dsp.2012.07.002.
Porzuczek, J. (2019). Assessment of the spatial distribution of moisture content in granular material using electrical impedance tomography. Sensors, 19(12), 2807. https://doi.org/10.3390/s19122807.
Taghizadeh-Tameh, J., Mousazadeh, H., Rafiee, S., & Tarabi, N. (2023). Development and evaluation of a slurry density measurement system based on Applied Current-Magnetic Induction Tomography
(AC-MIT). Flow Measurement and Instrumentation, 93, 102427. https://doi.org/10.1016/j.flowmeasinst.2023.102427.
Tan, C., Wu, Y., Xiao, Z., & Dong, F. (2018). Optimization of dual frequency-difference MIT sensor array based on sensitivity and resolution analysis. IEEE Access, 6, 34911-34920. https://doi.org/10.1109/ACCESS.2018.2849412.
Tarabi, N., Mousazadeh, H., Jafari, A., Taghizadeh-Tameh, J., & Kiapey, A. (2022). Experimental evaluation of some current injection-voltage reading patterns in electrical impedance tomography (EIT) and comparison to simulation results-case study: large scales. Flow Measurement and Instrumentation, 83, 102087. https://doi.org/10.1016/j.flowmeasinst.2021.102087.
Theraja, B. (2008). A textbook of electrical technology. ‎ Chand (S.) & Co Ltd, India.
Tong, G., Liu, S., & Liu, S. (2019). Computationally efficient image reconstruction algorithm for electrical capacitance tomography. Transactions of the Institute of Measurement and Control, 41(3), 631-646. http://dx.doi.org/10.1177/0142331218763013.
Vauhkonen, M., Vadász, D., Karjalainen, P. A., Somersalo, E., & Kaipio, J. P. (1998). Tikhonov regularization and prior information in electrical impedance tomography. IEEE transactions on medical imaging, 17(2), 285-293.
Wang, C., Liu, R., Fu, F., You, F., Shi, X., & Dong, X. (2007). Image reconstruction for magnetic induction tomography and preliminary simulations on a simple head model. IEEE Engineering in Medicine and Biology Society, 4406-4409.
Wang, M. (2022). Industrial tomography: systems and applications. 2nd Ed. Elsevier.
Wei, H. Y., Ma, L., & Soleimani, M. (2012). Volumetric magnetic induction tomography. Measurement Science and Technology, 23(5), 055401. http://doi.org/10.1088/0957-0233/23/5/055401.
Wei, K., Qiu, C. H., & Primrose, K. (2016). Super-sensing technology: Industrial applications and future challenges of electrical tomography. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 374, 20150328. http://doi.org/10.1098/rsta.2015.0328.
Wu, X. J., Xu, M. D., Li, C. D., Ju, C., Zhao, Q., & Liu, S. X. (2021). Research on image reconstruction algorithms based on autoencoder neural network of Restricted Boltzmann Machine (RBM). Flow Measurement and Instrumentation, 80, 102009. https://doi.org/10.1016/j.flowmeasinst.2021.102009.
Yang, W. Q., & Peng, L. (2002). Image reconstruction algorithms for electrical capacitance tomography. Measurement science and technology, 14(1), 1-13. https://doi.org/10.1088/0957-0233/14/1/201.
Yin, W., & Peyton, A. J. (2006). A planar EMT system for the detection of faults on thin metallic plates. Measurement Science and Technology, 17(8), 2130. https://doi.org/10.1088/0957-0233/17/8/011.
Yin, W., Chen, G., Chen, L., & Wang, B. (2011). The design of a digital magnetic induction tomography (MIT) system for metallic object imaging based on half cycle demodulation. IEEE Sensors Journal, 11(10), 2233-2240. https://doi.org/10.1109/JSEN.2011.2128866.
Zhao, X., Zhuang, H., Yoon, S. C., Dong, Y., Wang, W., & Zhao, W. (2017). Electrical impedance spectroscopy for quality assessment of meat and fish: A review on basic principles, measurement methods, and recent advances. Journal of Food Quality, 2017, 6370739. https://doi.org/10.1155/2017/6370739.