Magnetic induction tomography: experimental realization

Korjenevsky, A V; Черепенин, В. А.; Sapetsky, S A

doi:10.1088/0967-3334/21/1/311

Cited by 219 publications

(186 citation statements)

References 3 publications

(2 reference statements)

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“…Alternatively, it can also be evaluated via the steepest descent method using (4), where the step factor α can be computed via the relation given by:…”

Section: Trust Region Powell's Dog Leg Methodsmentioning

confidence: 99%

“…To date, the major progress reported in MIT image reconstruction mainly involved the application of linear algorithms including single step schemes such as linear back-projection [3,4], Tikhonov regularization [5][6][7][8][9][10], truncated singular value decomposition (TSVD) [9], and iterative schemes like simultaneous iterative reconstruction technique (SIRT) [9], Landweber method [11] and the conjugate gradients least squares (CGLS) method [12]. Single step reconstruction methods are fast but they can only produce qualitative images.…”

Section: Introductionmentioning

confidence: 99%

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Absolute Imaging of Low Conductivity Material Distributions Using Nonlinear Reconstruction Methods in Magnetic Induction Tomography

Dekdouk¹,

Ktistis²,

Armitage³

et al. 2016

PIER

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Abstract-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 < 5 Sm −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.

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“…Alternatively, it can also be evaluated via the steepest descent method using (4), where the step factor α can be computed via the relation given by:…”

Section: Trust Region Powell's Dog Leg Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Absolute Imaging of Low Conductivity Material Distributions Using Nonlinear Reconstruction Methods in Magnetic Induction Tomography

Dekdouk¹,

Ktistis²,

Armitage³

et al. 2016

PIER

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“…Magnetic induction tomography (MIT) is a new modality for medical, industrial and geophysical imaging [1][2][3], in which the impedance distribution within a medium is computed based on measurements of the mutual inductances between pairs of coils placed around the medium. One key advantage is its contact-less nature, which makes the technique of interest for non-invasive and non-intrusive applications.…”

Section: Introductionmentioning

confidence: 99%

Three-Dimensional Magnetic Induction Tomography Imaging Using a Matrix Free Krylov Subspace Inversion Algorithm

Wei¹,

Soleimani²

2012

PIER

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Abstract-Magnetic induction tomography (MIT) attempts to image the passive electromagnetic properties (PEP) of an object by measuring the mutual inductances between pairs of coils placed around its periphery. In recent years, there has been an increase in applications of non-contact magnetic induction tomography. When finite element-based reconstruction methods are used, that rely on the inversion of a derivative operator, the large size of the Jacobian matrix poses a challenge since the explicit formulation and storage of the Jacobian matrix could be in general not feasible. This problem is aggravated further in applications for example when the number of coils is increased and in three-dimension. Krylov subspace methods such as conjugate gradient (CG) methods are suitable for such large scale inverse problems. However, these methods require use of the Jacobian matrix, which can be large scale. This paper presents a matrix-free reconstruction method, that addresses the problems of large scale inversion and reduces the computational cost and memory requirements for the reconstruction. The idea behind the matrixfree method is that information about the Jacobian matrix could be available through matrix times vector products so that the creation and storage of big matrices can be avoided. Furthermore the matrix vector multiplications were performed in multiple core fashion so that the computational time can decrease even further. The method was tested for the simulated and experimental data from lab experiments, and substantial benefits in computational times and memory requirements have been observed.

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“…Magnetic induction tomography (MIT) [1], [2], [3] is a contact-less imaging modality which maps the electrical conductivity within a 3-D body. MIT requires an alternating magnetic excitation field to be coupled from an array of transmit coils (TXC) to the object.…”

Section: Introductionmentioning

confidence: 99%

“…20 MHz, [2], 10 MHz [1,4]. However, measurements in the β-dispersion range below 1 MHz are desirable because many pathological processes are reflected there [5].…”

Section: Introductionmentioning

confidence: 99%

Reduction of low-frequency noise in magnetic induction tomography systems

Scharfetter

Issa

2009

IFMBE Proceedings

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Abstract-Magnetic induction tomography (MIT) aims at the contact-less imaging of the electrical conductivity and related pathophysiological processes. Systems operating at frequencies below 1 MHz are desirable but suffer from poor SNR which can be dramatically impaired by mechanical vibrations. We present an algorithm which suppresses vibration noise in the imaginary part of the data and improves the SNR up to 35 dB.

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Magnetic induction tomography: experimental realization

Cited by 219 publications

References 3 publications

Absolute Imaging of Low Conductivity Material Distributions Using Nonlinear Reconstruction Methods in Magnetic Induction Tomography

Absolute Imaging of Low Conductivity Material Distributions Using Nonlinear Reconstruction Methods in Magnetic Induction Tomography

Three-Dimensional Magnetic Induction Tomography Imaging Using a Matrix Free Krylov Subspace Inversion Algorithm

Reduction of low-frequency noise in magnetic induction tomography systems

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