Solving the Inverse Problem of Magnetic Induction Tomography Using Gauss-Newton Iterative Method and Zoning Technique to Reduce Unknown Coefficients

TL;DR

This paper enhances Magnetic Induction Tomography (MIT) inverse problem solving by extending the Gauss-Newton method and employing zoning techniques, resulting in improved accuracy and reduced error in conductivity reconstruction.

Contribution

The paper introduces an extended Gauss-Newton iterative algorithm combined with zoning to effectively reduce unknown coefficients in MIT inverse problem solving.

Findings

Mean relative error rate reduced to 24.22%

Simulation results closely match real conductivity coefficients

Sensitivity matrices are effectively extracted under various conditions

Abstract

Magnetic Induction Tomography (MIT) is a promising modality for noninvasive imaging due to its contactless and nonionizing technology. In this imaging method, a primary magnetic field is applied by excitation coils to induce eddy currents in the material to be studied, and a secondary magnetic field is detected from these eddy currents using sensing coils. The image (spatial distribution of electrical conductivity) is then reconstructed using measurement data, the initial estimation of electrical conductivity, and the iterative solution of forward and inverse problems. The inverse problem can be solved using one-step linear, iterative nonlinear, and special methods. In general, the MIT inverse problem can be solved by Gauss- Newton iterative method with acceptable accuracy. In this paper, this algorithm is extended and the zoning technique is employed for the reduction of unknown coefficients. The simulation results obtained by the proposed method are compared with the real conductivity coefficients and the mean relative error rate is reduced to 24.22%. On the other hand, Gauss-Newton iterative method is extended for solving the inverse problem of the MIT, and sensitivity measurement matrices are extracted in different experimental and normalization conditions.