An Improved Image Reconstruction Algorithm Based on Iteration NR in Magnetic Induction Tomography
1 Faculty of Electronic Information and Electrical Engineering，Dalian University of Technology，Dalian 116023, Liaoning, China
2 Department of Neurology，First Affiliated Hospital of Dalian Medical University, Dalian 116011, Liaoning, China
Abstract：The image reconstruction process is a typical illposed problem in magnetic induction tomography (MIT), in which the numerical solution is unstable. To solve this problem, an improved iteration NewtonRaphson algorithm based on weighted matrix and L1norm regularization is improved. The proposed method adds the weight matrix in the objective function and adds L1norm regularization term in L2norm regularization penalty term. The analysis is made for three typical models in the data with and without noise, respectively. And the proposed algorithm is contrasted with Tikhonov regularization algorithm and iterative NR algorithm. In the data without noise, relative to Tikhonov regularization algorithm and iterative NR algorithm, the relative error is reduced by 011-014. And then, the correlation coefficient is raised by 13%-17%. The algorithm has good performance in imaging. In the data with noise, the relative error is reduced by 006-009, and the correlation coefficient is raised by 7%-10% in the proposed algorithm. The algorithm has good antinoise performance, which has offered theory basis for the study of reconstruction accuracy.
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