Determination of sensitive frequency margin for aggregated protein concentration quantification by fd-electrical impedance tomography

Sifuna, Martin Wekesa; Baidillah, Marlin Ramadhan; Kawashima, Daisuke; Darma, Panji Nursetia; Odari, Eddy Okoth; Takei, Masahiro

doi:10.1016/j.measurement.2021.110135

Cited by 6 publications

(1 citation statement)

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“…EIT reconstruction is highly nonlinear and ill-posed due to the limited number of available observations. 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.…”

Section: Introductionmentioning

confidence: 99%

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

Wang,

Sun,

2024

Sensors

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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.

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