scholarly journals The application of a priori structural information based regularization in image reconstruction in magnetic induction tomography

2010 ◽  
Vol 224 ◽  
pp. 012048
Author(s):  
B Dekdouk ◽  
C Ktistis ◽  
W Yin ◽  
D W Armitage ◽  
A J Peyton
Keyword(s):  
Image Reconstruction ◽  
Magnetic Induction ◽  
Structural Information ◽  
A Priori ◽  
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.

Molten Metal Flow Visualization Using Dynamical Magnetic Induction Tomography

2008 ◽  
Author(s):  
Manuchehr Soleimani
Keyword(s):  
Image Reconstruction ◽  
Magnetic Induction ◽  
Metal Flow ◽  
Real Data ◽  
Reconstruction Technique ◽  
Casting Unit ◽  
Image Reconstruction Technique ◽  
Dynamical Method ◽  
Magnetic Induction Tomography ◽  
Using Data

Magnetic induction tomography (MIT) is a contactless electromagnetic imaging. In our previous study we have demonstrated that the MIT is able to image metal flow in continous casting. In this paper we demonstrate application of a Kalman filter for the image reconstruction problem of MIT using real data from a continous casting unit. The proposed method is using data from each excitation to reconstruct the conductivity distribution. We discuss the advantage of the dynamical method compared to traditional static image reconstruction technique.

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