Abstract
Magnetic detection electrical impedance tomography (MD-EIT) can be used to reconstruct images of conductivity from magnetic field measurement taken around the body in vivo. To achieve the lung tumor imaging based on the MD-EIT, this study established the human thoracic model according to CT image. The Moore-Penrose inverse, TSVD and Tikhonov regularization were tried before the reconstruction to improve imaging accuracy respectively. Four levels of noise were added into the simulation data to meet the real detection situation (the SNR is 30, 60, 90 and 120 dB). The reconstruction results, which were pre-processed by TSVD regularization, had the best performance. The average relative error (ARE) values of current density distribution equals 0.21 and 0.22 for healthy and lung tumor person respectively. The tumor in lung can be distinguished clearly from the MD-EIT image. The MD-EIT is one of the most promising technology in dynamic lung imaging for clinical application.
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Acknowledgements
We thank Dr. Guangxu Li, Dr. Yu Zheng, Lei Tian their help in field measurements and programming. This study financially supported by the 61th Batch of China Postdoctoral Science Foundation and Key Laboratory of Biomedical Effects of Nanomaterials and Nanosafety, CASCNSKF 2016027.
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Wang, H., Feng, Y., Wang, J., Qi, H., Zhao, Z., Chen, R. (2018). The Research on the Lung Tumor Imaging Based on the Electrical Impedance Tomography. In: Lu, H., Xu, X. (eds) Artificial Intelligence and Robotics. Studies in Computational Intelligence, vol 752. Springer, Cham. https://doi.org/10.1007/978-3-319-69877-9_11
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