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Sensing and Imaging

, 20:27 | Cite as

Simulation Research of Impact of Number of Coils in EMT Sensors on Reconstructed Images Quality

  • Xianglong Liu
  • Ze LiuEmail author
  • Yuanli Yue
Original Paper
  • 79 Downloads
Part of the following topical collections:
  1. Recent Developments in Sensing and Imaging

Abstract

Electromagnetic tomography (EMT) has been developed for visualizing the conductivity distribution of materials with multi-coil electromagnetic sensors. It is crucial to design an optimized EMT sensor for the improvement of reconstructed images quality. The selection of the number of coils is discussed in this paper due to its great importance to system performance and system complexity. It is commonly believed that more coils in EMT sensor would obtain better performance of reconstructed images. In order to study the impact of number of coils in EMT sensors on quality of reconstructed images, five kinds of sensors with different number of coils including 4, 8, 12, 16 and 20, are involved to conduct numerical simulations. EMT forward problem can be solved through implementing finite element method, then measurements and sensitivity matrix are obtained, which can be used to solve EMT inverse problem with proper image reconstruction algorithms. Five typical conductivity distributions are used to verify the performance of EMT sensors with different number of coils. The sensitivity matrices of different EMT sensors are analyzed to further explain the essential reason of these numerical simulation results using singular value decomposition. It can be concluded that EMT sensor with 16 coils produces good-enough image reconstruction results for most of the typical conductivity distributions. Limited improvement can be obtained in the quality of reconstructed images when the number of coils is more than 16.

Keywords

Electromagnetic tomography Image reconstruction Conjugate gradient Landweber iteration Inverse problem Singular value decomposition (SVD) 

Notes

Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant 61771041.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Electronic and Information EngineeringBeijing Jiaotong UniversityBeijingChina

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