volume | PIER Journals

Abstract

DBIM is a deterministic iterative method which exhibits second-order convergence indicating that the reconstruction error decreases quadratically with successive iterations. Existing regularization techniques when applied with DBIM often face challenges in determining the optimal regularization parameter (λ), leading to inconsistent convergence across various problems. To address this, a quantitative imaging algorithm is proposed in this paper by combining the Distorted Born Iterative Method (DBIM) and Hybrid LSQR method for solving Inverse Scattering Problems (ISP). This enhances the accuracy of the reconstructed object profiles and optimizes the regularization level to prevent both under- and over-regularization. For a fair comparison with the results in the literature, simulation studies are conducted using a breast profile that has two tumor inclusions, each with a radius of 6 mm, and two fibro-glandular tissue inclusions, each with a radius of 10 mm. The proposed method achieves a Root Mean Square Error (RMSE) of 0.75, indicating a better level of accuracy. The experimental validation is performed using a phantom made of Delrin material. The Delrin phantom, with a diameter of 10 cm, contains three inclusions made of PVC material with diameters of 10 mm, 6 mm and 3 mm. These inclusions have been successfully reconstructed with errors 0.085, 0.128 and 0.165 respectively. These results demonstrate the effectiveness of this algorithm in reconstructing both high and low-dielectric profiles, making it suitable for microwave imaging applications.

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Ms. Soumya Nharakkat

Mr. Thathamkulam Anjit

Ms. Anju Maria

Prof. Palayyan Mythili