Magnetic Induction Tomography Image Reconstruction Research Articles

ABSOLUTE IMAGING OF LOW CONDUCTIVITY MATERIAL DISTRIBUTIONS USING NONLINEAR RECONSTRUCTION METHODS IN MAGNETIC INDUCTION TOMOGRAPHY

Magnetic Induction Tomography (MIT) is a newly developing technique of electrical tomography that in principle is able to map the electrical conductivity distribution in the volume of objects. The image reconstruction problem in MIT is similar to electrical impedance tomography (EIT) in the sense that both seek to recover the conductivity map, but differ remarkably in the fact that data being inverted in MIT is derived from induction theory and related sources of noise are different. Progress in MIT image reconstruction is still limited, and so far mainly linear algorithms have been implemented. In difference imaging, step inversion was demonstrated for recovering perturbations within conductive media, but at the cost of producing qualitative images, whilst in absolute imaging, linear iterative algorithms have mostly been employed but mainly offering encouraging results for imaging isolated high conductive targets. In this paper, we investigate the possibility of absolute imaging in 3D MIT of a target within conductive medium for low conductivity application ( σ< 5S m −1 ). For this class of problems, the MIT image reconstruction exhibits non-linearity and ill-posedness that cannot be treated with linear algorithms. We propose to implement for the first time in MIT two effective inversion methods known in non-linear optimization as Levenberg Marquardt (LMM) and Powell's Dog Leg (PDLM) methods. These methods employ damping and trust region techniques for controlling convergence and improving minimization of the objective function. An adaptive version of Gauss Newton is also presented (AGNM), which implements a damping mechanism to the regularization parameter. Here, the level of penalty is varied during the iterative process. As a comparison between the methods, different criteria are examined from image reconstructions using the LMM, PDLM and AGNM. For test examples, volumetric image reconstruction of a perturbation within homogeneous cylindrical background is considered. For inversion, an independent finite element FEM software package Maxwell by Ansys is employed to generate simulated data using a model of a 16 channel MIT system. Numerical results are employed to show different performance characteristics between the methods based on convergence, stability and sensitivity to the choice of the regularization parameter. To demonstrate the effect of scalability of absolute imaging in MIT for more realistic problems, a human head model with an internal anomaly is used to produce reconstructions for different finer resolutions. AGNM is adopted here and employs the Krylov subspace method to replace the computationally demanding direct inversion of the regularized Hessian.

Simulation study for a new magnetic induction tomography coil system with weakly perturbing in conducting background.

Biomedical magnetic induction tomography (MIT) aims to reconstruct the passive electrical properties within biological tissues, especially the electrical conductivity. A weak perturbation inside a conducting object is put in the improved MIT coil system which uses the two-arm Archimedean spiral coil as the excitation coil and the circular coil as the receiver coil. The forward problem for this model is calculated by three-dimension electromagnetic simulation experiments. Under the different simulation conditions, the phase shift of voltage induced in the receiver coil is compared with that for the common model using the circular coil as the excitation and receiver coil. The results show that the sensitivity to the improved model is much higher than that to the common model except for the case that the perturbation appears in y-axis, which effectively confirms the previous conclusions and indicates that the improved coil system has the potential advantage for MIT image reconstruction.

An Improved Back-Projection Algorithm for Magnetic Induction Tomography Image Reconstruction

Magnetic induction tomography (MIT) acted as a contactless and non-invasive medical imaging technology has aroused wide concern, while a large amount of calculation and a series of convergence problems in the solution of the inverse problem become technical difficulties for MIT. In order to solve these problems, an improved back-projection image reconstruction algorithm based on the magnetic field lines distribution is presented in this paper. Firstly, the eddy current problem of MIT was solved by the finite element method to obtain the magnetic field distribution. Secondly, the back-projection areas were divided according to the magnetic field lines distribution in the homogeneous field. Finally, image reconstruction was realized by projecting the phase shifts back along the corresponding projection area. The reconstruction results for perturbations with different conductivities appearing at different locations reveal that the improved back-projection algorithm for MIT owning the character of high speed performs well in reflecting location and shape information of the perturbation.

A New Hybrid Image Reconstruction Algorithm for Magnetic Induction Tomography

A new hybrid algorithm is presented in this paper, which solves the ill-posed inverse problem of magnetic induction tomography (MIT) and improves the quality of reconstructed image. The hybrid algorithm firstly produces the preliminary image region using Tikhonov regularization algorithm, and then it obtains the final reconstructed image using variation regularization algorithm. The hybrid algorithm, compared with the Tikhonov regularization algorithm and the variation regularization algorithm, overcomes the numerical instability of MIT image reconstruction and accelerates the convergence speed of image reconstruction, and it also improves the resolving power of targets conductor and the quality of the reconstructed image. Simulation results show that the quality of the reconstructed image obtained using the hybrid algorithm is enhanced, so an effective algorithm for magnetic induction tomography (MIT) is introduced.

Numerical modeling of magnetic induction tomography using the impedance method

This article discusses the impedance method in the forward calculation in magnetic induction tomography (MIT). Magnetic field and eddy current distributions were obtained numerically for a sphere in the field of a coil and were compared with an analytical model. Additionally, numerical and experimental results for phase sensitivity in MIT were obtained and compared for a cylindrical object in a planar array of sensors. The results showed that the impedance method provides results that agree very well with reality in the frequency range from 100 kHz to 20 MHz and for low conductivity objects (10 S/m or less). This opens the possibility of using this numerical approach in image reconstruction in MIT.

Imaging artifacts in magnetic induction tomography caused by the structural incorrectness of the sensor model

Magnetic induction tomography (MIT) is a noninvasive imaging modality that aims to reconstruct the interior electrical conductivity distribution of the human body. It uses magnetic induction to excite eddy currents in the body and an array of sensor coils to detect the perturbations in the magnetic field. Image reconstruction in MIT is usually carried out by minimizing the residuals between the estimated and measured quantities assuming a structurally correct model. Thus, any mismatch between the simulated and the true experimental coil setup alters the data and may cause artifacts in the images. In this paper, a simulation study was performed to investigate the effect of modeling mismatches on measurements and corresponding reconstructed images. It was found that slight distortions of the receivers may cause up to 20% deviations in the data considering a local and small perturbation in conductivity. Unless the geometry is modeled correctly, these artifacts may spoil the images particularly for the case of flexible systems that have many degrees of freedom and systems that require different adjustments for different imaging sessions. If the system does not need calibration, for instance as in the case of head applications, then a rigid mechanical support appears to be an important design issue to achieve a better image quality.

Sensitivity maps in three-dimensional magnetic induction tomography

Magnetic induction tomography (MIT) attempts to image the electromagnetic characteristics of air object by measuring the mutual inductances between pairs of coils placed around its periphery. MIT is a multiple coil eddy current testing method, so the technique developed in conventional MIT systems can easily be adapted for general eddy current NDT For the image reconstruction in MIT one needs to solve the forward problem as well as the sensitivity maps. In this paper we present three-dimensional sensitivity maps for permeability and conductivity, calculated based on an edge-based finite element software developed for MIT image reconstruction. The differences between sensitivity maps for conductivity and permeability have been studied. The sensitivity maps presented in this paper are the fundamental bases for the image reconstruction in MIT.

MIT image reconstruction based on edge-preserving regularization

Tikhonov regularization has been widely used in electrical tomography to deal with the ill-posedness of the inverse problem. However, due to the fact that discontinuities are strongly penalized, this approach tends to produce blurred images. Recently, a lot of interest has been devoted to methods with edge-preserving properties, such as those related to total variation, wavelets and half-quadratic regularization. In the present work, the performance of an edge-preserving regularization method, called ARTUR, is evaluated in the context of magnetic induction tomography (MIT). ARTUR is a deterministic method based on half-quadratic regularization, where complementary a priori information may be introduced in the reconstruction algorithm by the use of a nonnegativity constraint. The method is first tested using an MIT analytical model that generates projection data given the position, the radius and the magnetic permeability of a single nonconductive cylindrical object. It is shown that even in the presence of strong Gaussian additive noise, it is still able to recover the main features of the object. Secondly, reconstructions based on real data for different configurations of conductive nonmagnetic cylindrical objects are presented and some of their parameters estimated.