scholarly journals Advancements in Transmitters and Sensors for Biological Tissue Imaging in Magnetic Induction Tomography

Sensors ◽  
2012 ◽  
Vol 12 (6) ◽  
pp. 7126-7156 ◽  
Author(s):  
Zulkarnay Zakaria ◽  
Ruzairi Abdul Rahim ◽  
Muhammad Saiful Badri Mansor ◽  
Sazali Yaacob ◽  
Nor Muzakkir Nor Ayob ◽  
...  
Keyword(s):  
Magnetic Induction ◽  
Biological Tissue ◽  
Tissue Imaging ◽  
Magnetic Induction Tomography

scholarly journals A Novel Algorithm for High-Resolution Magnetic Induction Tomography Based on Stacked Auto-Encoder for Biological Tissue Imaging

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 185597-185606
Author(s):  
Ruijuan Chen ◽  
Juan Huang ◽  
Huiquan Wang ◽  
Bingnan Li ◽  
Zhe Zhao ◽  
...  
Keyword(s):  
High Resolution ◽  
Magnetic Induction ◽  
Biological Tissue ◽  
Tissue Imaging ◽  
Magnetic Induction Tomography ◽  
Novel Algorithm

scholarly journals Simulation of Single Channel Magnetic Induction Tomography for Meningitis Detection By Using COMSOL Multiphysics

2021 ◽  
Vol 2071 (1) ◽  
pp. 012039
Author(s):  
Aiman Abdulrahman Ahmed ◽  
Zulkarnay Zakaria ◽  
Marwah Hamood Ali ◽  
Anas Mohd Noor ◽  
Siti Fatimah Binti Abdul Halim ◽  
...  
Keyword(s):  
Magnetic Induction ◽  
Single Channel ◽  
Tissue Imaging ◽  
Comsol Multiphysics ◽  
Optimum Frequency ◽  
Shift Measurement ◽  
Property Distribution ◽  
Conductivity Property ◽  
Magnetic Induction Tomography ◽  
Brain Tissues

Abstract Meningitis is a inflammation of the meninges and the most common central nervous system (CNS) due to bacterial infection. Numbers of children who have bacterial meningitis are still high in recent 15 years regardless of the availability of newer antibiotics and preventive strategies. This research focuses on simulation using COMSOL Multiphysics on the design and development of magnetic induction tomography (MIT) system that emphasizes on a single channel rotatable of brain tissue imaging. The purpose of this simulation is to test the capability of the developed MIT system in detecting the change in conductivity and to identify the suitable transmitter-receiver pair and the optimum frequency based on phase shift measurement technique for detecting the conductivity property distribution of brain tissues. The obtained result verified that the performance of the square coil with 12 number of turns (5Tx-12Rx) with 10MHz frequency has been identified as the suitable transmitter-receiver pair and the optimum frequency for detecting the conductivity property distribution of brain tissues.

Inspection of defects in CFRP by improved magnetic induction tomography

2019 ◽  
Vol 61 (3) ◽  
pp. 255-259
Author(s):  
Lipan Zhang ◽  
Qifeng Meng ◽  
Kai Song ◽  
Ming Gao ◽  
Zhiyuan Cheng
Keyword(s):  
Magnetic Induction ◽  
Magnetic Induction Tomography

Image Reconstruction with the Fourier Coefficients for Magnetic Induction Tomography

2020 ◽  
Vol 16 (2) ◽  
pp. 156-163
Author(s):  
Jingwen Wang ◽  
Xu Wang ◽  
Dan Yang ◽  
Kaiyang Wang
Keyword(s):  
Inverse Problem ◽  
Image Reconstruction ◽  
Magnetic Induction ◽  
Fourier Coefficients ◽  
Signal Reconstruction ◽  
Reconstruction Algorithm ◽  
Reconstruction Method ◽  
Measurement Equation ◽  
Image Reconstruction Method ◽  
Magnetic Induction Tomography

Background: Image reconstruction of magnetic induction tomography (MIT) is a typical ill-posed inverse problem, which means that the measurements are always far from enough. Thus, MIT image reconstruction results using conventional algorithms such as linear back projection and Landweber often suffer from limitations such as low resolution and blurred edges. Methods: In this paper, based on the recent finite rate of innovation (FRI) framework, a novel image reconstruction method with MIT system is presented. Results: This is achieved through modeling and sampling the MIT signals in FRI framework, resulting in a few new measurements, namely, fourier coefficients. Because each new measurement contains all the pixel position and conductivity information of the dense phase medium, the illposed inverse problem can be improved, by rebuilding the MIT measurement equation with the measurement voltage and the new measurements. Finally, a sparsity-based signal reconstruction algorithm is presented to reconstruct the original MIT image signal, by solving this new measurement equation. Conclusion: Experiments show that the proposed method has better indicators such as image error and correlation coefficient. Therefore, it is a kind of MIT image reconstruction method with high accuracy.

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