A regularization structure based on novel iterative penalty term for electrical impedance tomography

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

“…(b) Tank experiment data: Static tank experiments have been carried out in previous work [ 21 ]. Data were collected by the TJU-EIT system under adjacent measurement strategy, using the injection current of 5 mA and frequency of 100 kHz.…”

“…Given this constraint, the solution is extremely sensitive to small perturbations caused by measurement noise and modeling errors, resulting in inherently low spatial resolution and instability in the reconstructed images [ 11 ]. In order to address this problem, many traditional imaging algorithms have been developed, including direct sensitivity coefficient method [ 12 ], Landweber-type algorithms [ 13 , 14 ], gradient algorithms [ 15 , 16 ], Newton algorithms [ 17 , 18 ], regularization algorithms [ 19 , 20 , 21 ], etc. In recent years, owing to the outstanding ability to solve nonlinear problems, deep learning has received widespread attention from academics [ 22 , 23 , 24 ].…”

“…proposed an adaptive norm regularization form, which preserves geometric shapes and captures small objects [ 30 ]. In previous work, an advanced penalty term was proposed to optimize the regularization method and further improve the reconstruction accuracy [ 21 ]. These algorithms can provide stable solutions, but they cannot achieve an appropriate balance of accuracy, stability, and efficiency.…”

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

“…For scenarios that extend beyond this predefined range, alternative considerations are necessary. In cases where the subject is free from disease or where only two phases of conductivity are present, traditional EIT reconstruction methods or their advanced patterns are typically sufficient [ 30 ].…”

“…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.…”