Solving the MIT Inverse Problem by Considering Skin and Proximity Effects in Coils
Hassan Yazdanian, Reza Jafari, Hamid Abrishami Moghaddam

TL;DR
This paper enhances magnetic induction tomography by incorporating skin and proximity effects in coils, improving the accuracy of conductivity reconstructions in low-conductivity regions.
Contribution
It introduces an improved forward modeling technique that accounts for coil effects and employs a regularized Gauss-Newton algorithm for better conductivity imaging.
Findings
Improved forward method yields more accurate conductivity reconstructions.
Inclusion of coil effects is crucial for low-conductivity region imaging.
Enhanced technique outperforms previous models in synthetic phantom tests.
Abstract
This paper presents an improved technique for solving the inverse problem in magnetic induction tomography (MIT) by considering skin and proximity effects in coils. MIT is a non-contact, noninvasive, and low-cost imaging modality for obtaining the distribution of conductivity inside an object. Reconstruction of low conductivity distribution by MIT requires more accurate techniques since measured signals are inherently weak and the reconstruction problem is highly nonlinear and ill-posed. Previous MIT inverse problem studies have ignored skin and proximity effects inside coils in the forward method. In this article, the improved technique incorporates these effects in the forward method. Furthermore, it employs the regularized Gauss-Newton algorithm to reconstruct the conductivity distribution. The regularization parameter is obtained by an adaptive method using the two input parameters:…
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