A new image reconstruction algorithm for real-time monitoring of conductivity and permeability changes in Magnetic Induction Tomography

2012 ◽  
Author(s):  
J. Caeiros ◽  
R. C. Martins ◽  
B. Gil
Keyword(s):  
Image Reconstruction ◽  
Real Time ◽  
Magnetic Induction ◽  
Reconstruction Algorithm ◽  
Real Time Monitoring ◽  
Image Reconstruction Algorithm ◽  
Permeability Changes ◽  
Magnetic Induction Tomography

SENSITIVITY MAPS RECONSTRUCTION FOR MAGNETIC INDUCTION TOMOGRAPHY MODALITY USING EXPERIMENTAL TECHNIQUE

2015 ◽  
Vol 77 (17) ◽  
Author(s):  
Zulkarnay Zakaria ◽  
Hafizi Suki ◽  
Masturah Tunnur Mohamad Talib ◽  
Ibrahim Balkhis ◽  
Maliki Ibrahim ◽  
...  
Keyword(s):  
Finite Element ◽  
Image Reconstruction ◽  
Experimental Technique ◽  
Magnetic Induction ◽  
Reconstruction Algorithm ◽  
Finite Element Technique ◽  
Image Reconstruction Algorithm ◽  
Magnetic Induction Tomography ◽  
Element Technique ◽  
Sensitivity Maps

Magnetic induction tomography (MIT) is a relatively new non-contacting technique for visualization of passive electrical property distribution inside a media. In any tomography system, the image is reconstructed using image reconstruction algorithm which requires sensitivity maps. There are three methods of acquiring sensitivity maps; finite element technique, analytically or experimentally. This research will focus on the experimentally method. Normally sensitivity map is generates using finite element technique that usually ignore certain parameters in real setup which in turn contribute to errors or blur in the reconstructed image. Thus experimental technique needs to be explored as an improvement as it is based on real parameters exists in the experimental setup. This paper starts with general view of magnetic induction tomography, image reconstruction algorithm and finally on the experimental technique of generating sensitivity maps.

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.

Simulative Study of Two-Phase Homogenous and Isotropic Media Imaging using Magnetic Induction Tomography

2014 ◽  
Vol 69 (8) ◽  
Author(s):  
Zulkarnay Zakaria ◽  
Ibrahim Balkhis ◽  
Lee Pick Yern ◽  
Nor Muzakkir Nor Ayob ◽  
Mohd Hafiz Fazalul Rahiman ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Magnetic Induction ◽  
Optical Tomography ◽  
Reconstruction Algorithm ◽  
Projection Algorithm ◽  
Back Projection ◽  
Two Phase ◽  
Excitation Coil ◽  
Magnetic Induction Tomography ◽  
Linear Back Projection

Magnetic induction tomography is a new non-invasive technology, based on eddy current discovery of electromagnetic induction by Michael Faraday. Through this technique, the passive electrical properties distribution of an object can be obtained by the use of image reconstruction algorithm implemented in this system. There are many types of image reconstruction that have been developed for this modality, however in this paper only two algorithms discussed, Linear Back Projection and Eminent Pixel Reconstruction. Linear Back Projection algorithm is the most basic type of image reconstruction. It is the simplest and fast algorithm out of all types of algorithms, whereas Eminent Pixel Reconstruction algorithm is an improved algorithm which provided better images and has been implemented in other modalities such as optical tomography. This paper has implemented Eminent Pixel Reconstruction in magnetic induction tomography applications and the performance is compared to Linear Back Projection based on the simulation of the fourteen types of simulated phantoms of homogenous and isotropic conductivity property. It was found that Eminent Pixel Reconstruction has produced better images relative to Linear Back Projection, however the images are still poor when the objects are located near to the excitation coil or sensor and it is worse when the distance between objects are near to each other.

MICROWAVE IMAGING OF EMBEDDED OBJECT USING AN ENHANCED IMAGE RECONSTRUCTION ALGORITHM

2015 ◽  
Vol 74 (20) ◽  
pp. 1793-1801
Author(s):  
Sidi Mohammed Chouiti ◽  
Lotfi Merad ◽  
Sidi Mohammed Meriah ◽  
Xavier Raimundo
Keyword(s):  
Image Reconstruction ◽  
Reconstruction Algorithm ◽  
Microwave Imaging ◽  
Image Reconstruction Algorithm
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