Posterior Approximate Clustering-Based Sensitivity Matrix Decomposition for Electrical Impedance Tomography

Abstract

This paper introduces a sensitivity matrix decomposition regularization (SMDR) method for electric impedance tomography (EIT). Using k-means clustering, the EIT-reconstructed image can be divided into four clusters, derived based on image features, representing posterior information. The sensitivity matrix is then decomposed into distinct work areas based on these clusters. The elimination of smooth edge effects is achieved through differentiation of the images from the decomposed sensitivity matrix and further post-processing reliant on image features. The algorithm ensures low computational complexity and avoids introducing extra parameters. Numerical simulations and experimental data verification highlight the effectiveness of SMDR. The proposed SMDR algorithm demonstrates higher accuracy and robustness compared to the typical Tikhonov regularization and the iterative penalty term-based regularization method (with an improvement of up to 0.1156 in correlation coefficient). Moreover, SMDR achieves a harmonious balance between image fidelity and sparsity, effectively addressing practical application requirements.

Layered Fusion Reconstruction Based on Fuzzy Features for Multi-Conductivity Electrical Impedance Tomography

“…Linearization methods for EIT are time-efficient, stable, and simple, making them prevalent in clinical practice [ 26 , 27 , 28 , 29 ]. Recent studies have demonstrated that the reconstruction accuracy of two-phase distributions, based on regularization algorithms, can be significantly enhanced through simple post-processing or a few iterative steps [ 30 , 31 ]. However, for the distribution of three or more phases, specifically in detecting local lung anomalies, it is still difficult for these methods to reconstruct conductivity distributions efficiently.…”