Simulation research of impact of number of coils in EMT sensors on reconstructed images quality

EasyChair Preprint 693

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 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 (FEM), 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 (SVD). It can be concluded that EMT sensor with 16 coils produces the best 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.

Keyphrases: Landweber iteration, conjugate gradient, electromagnetic tomography, image reconstruction

@booklet{EasyChair:693,
  author    = {Xianglong Liu and Ze Liu and Yuanli Yue},
  title     = {Simulation research of impact of number of coils in EMT sensors on reconstructed images quality},
  doi       = {10.29007/h5vs},
  howpublished = {EasyChair Preprint 693},
  year      = {EasyChair, 2018}}