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.

A Data Standardization and Reconstruction Method for Magnetic Induction Tomography

2013 ◽  
Vol 647 ◽  
pp. 560-565 ◽  
Author(s):  
Qiang Du ◽  
Bao Dong Bai ◽  
Li Ke
Keyword(s):  
Eddy Current ◽  
Magnetic Induction ◽  
Impedance Measurement ◽  
Reconstruction Algorithm ◽  
Biological Tissues ◽  
Measured Data ◽  
Image Resolution ◽  
Reconstruction Method ◽  
Data Standardization ◽  
Magnetic Induction Tomography

Magnetic induction tomography (MIT) is a biologic tomography technology, which is to obtain the conductivity distribution by detecting the data on the boundary of the imaging area based on the eddy current principle. The small impedance difference between biological tissues makes the eddy current weak, and it leads to a direct effect on the biological impedance measurement and imaging sensitivity. A measured data standardization method is presented in this paper for enhancing the measured data sensitivity, and combined with the back-projection reconstruction algorithm to get reconstruction image. It is applied to a variety of measurement and the simulation experiment based on the calculation results of finite-element methods. The reconstructed images indicate that the method can improve the image resolution and sensitivity, and which provides an effective data standardization and reconstruction algorithm for the magnetic induction tomography.

scholarly journals A Lagrange-Newton Method for EIT/UT Dual-Modality Image Reconstruction

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 1966 ◽  
Author(s):  
Guanghui Liang ◽  
Shangjie Ren ◽  
Shu Zhao ◽  
Feng Dong
Keyword(s):  
Inverse Problem ◽  
Image Reconstruction ◽  
Newton Method ◽  
Constraint Equation ◽  
Reconstruction Method ◽  
Ultrasound Tomography ◽  
Impedance Tomography ◽  
Dual Modality ◽  
Mesh Model ◽  
Image Reconstruction Method

An image reconstruction method is proposed based on Lagrange-Newton method for electrical impedance tomography (EIT) and ultrasound tomography (UT) dual-modality imaging. Since the change in conductivity distribution is usually accompanied with the change in acoustic impedance distribution, the reconstruction targets of EIT and UT are unified to the conductivity difference using the same mesh model. Some background medium distribution information obtained from ultrasound transmission and reflection measurements can be used to construct a hard constraint about the conductivity difference distribution. Then, the EIT/UT dual-modality inverse problem is constructed by an equality constraint equation, and the Lagrange multiplier method combining Newton-Raphson iteration is used to solve the EIT/UT dual-modality inverse problem. The numerical and experimental results show that the proposed dual-modality image reconstruction method has a better performance than the single-modality EIT method and is more robust to the measurement noise.

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.

scholarly journals The non-Fourier image reconstruction method for the STIX instrument

2020 ◽  
Vol 29 (1) ◽  
pp. 220-230
Author(s):  
Marek Siarkowski ◽  
Tomasz Mrozek ◽  
Janusz Sylwester ◽  
Michalina Litwicka ◽  
Magdalena Dąbek
Keyword(s):  
Image Reconstruction ◽  
Reconstruction Algorithm ◽  
Point Sources ◽  
Detector Response ◽  
Reconstruction Method ◽  
The Past ◽  
Image Reconstruction Method ◽  
Real Geometry ◽  
First Time ◽  
Single Pixel

AbstractIn this work we aimed to develop the image reconstruction algorithm without any analytical simplifications and restrictions. In our method we abandon Fourier’s approach to image reconstruction, and instead use the number of counts recorded in each detector pixel, and then reconstruct each image using a classical Richardson-Lucy algorithm. Among similar works performed in the past, our approach is based, for the first time, on the real geometry of STIX. We made a preliminary analysis of expected differences in STIX imaging which may occur due to usage of slightly different geometries. The other difference is that we use single-pixel-response maps. Namely, knowing the instrument geometry we are able to calculate the detector response for point sources covering entire the solar disc. Next, we iteratively combine them with varying weights until the best match between reconstructed and observed detector responses is achieved. Preliminary tests revealed that the developed algorithm reproduces high quality images. The algorithm is moderately fast, but the result comparable to CLEAN algorithm is obtained within 20-50 iteration steps which takes less than 2 seconds on typical portable computer configuration. The location, size and intensity of reconstructed sources are very close to simulated ones. Therefore the algorithm is very well suited for the detailed photometry of the solar HXR sources. Moreover, its simplicity allows to improve photon transmission calculation in case of any grids uncertainties measured after the launch.

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