Electrical Impedance Tomography imaging using Gauss-Newton algorithm

Islam, Md. Rabiul; Kiber, Md. Adnan

doi:10.1109/iciev.2014.6850719

Cited by 16 publications

(6 citation statements)

References 10 publications

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“…We emphasize that these simulations employed the Gauss-Newton 1-step inversion, the most commonly used for tactile sensing, with the Laplace prior, the associated default in EIDORS, which influence how noise affects the image (for examples of the effects of other algorithms see e.g. [29,30]). Figure 2 shows how, for a given set of simulation parameters, an image first loses fidelity and eventually breaks up completely as the noise amplitude increases.…”

Section: Resultsmentioning

confidence: 99%

EIT for tactile sensing: considering artefacts in hyperparameter selection

Smela

2022

Eng. Res. Express

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Electrical impedance tomography (EIT) is employed in tactile sensing to create an image of impedance changes within a continuous sensor using electrodes placed only at the perimeter. Noise destabilizes EIT images, and the onset of instability is associated with the appearance of artefacts, which are spurious image features that are not associated with sensor responses to contacts. Artefacts are detrimental because the essential features of contacts, or targets, must be correctly represented, including how many there are and their approximate shapes and locations, yet their presence has not previously been used as a performance measure. Regularization, the extent of which is determined by the hyperparameter λ, is used to manage the destabilization, but it results in spatially non-uniform defocusing of image features. We therefore introduce an efficient criterion for evaluating tactile sensor image quality based on the onset of artefacts. Using simulated data and the one-step Gauss-Newton reconstruction algorithm with the Laplace prior, the noise level at which artefacts first appear at a given hyperparameter, or noise threshold Nth(λ), is found. How the relationship depends on target characteristics and other factors is shown, and Nth can vary by orders of magnitude. The conceptually similar BestRes method and the classical L-curve and generalized cross-validation (GCV) methods for determining an optimal hyperparameter are evaluated using the criterion of artefact-free images. The L-curve generates hyperparameters that are well matched to the onset of artefacts, except at high noise; the other two result in artefacts. For high dynamic range tactile inputs, setting the threshold at a fixed value using a method such as Nth is not advisable, and automatic regularization tailored to the input may be needed using a method such as the L-curve or GCV, provided that the computational overhead is tolerable.

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Section: Resultsmentioning

confidence: 99%

EIT for tactile sensing: considering artefacts in hyperparameter selection

Smela

2022

Eng. Res. Express

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“…Considering the fact that skin is a highly resistive material and the human body has a complex conductivity distribution, we first apply the nonlinear absolute Gauss-Newton imaging [ 19 ], where we set σ m based on reference conductivity data [ 16 ] for each body parts such that the muscle is the most conductive, which is followed by the fat and the bone in order. We choose the Laplace prior in which the regularization matrix is interpreted as a 2 nd order High Pass Filter (HPF) [ 20 ].…”

Section: Methodsmentioning

confidence: 99%

Investigation of physiological swelling on conductivity distribution in lower leg subcutaneous tissue by electrical impedance tomography

Ogawa

Baidillah

Akita

et al. 2020

Journal of Electrical Bioimpedance

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There is a strong need for a non-invasive measurement technique that is capable of accurately identifying the physiological condition change or heterogeneity of subcutaneous adipose tissue (SAT) by localizing the abnormalities within the compartment. This paper aims to investigate the feasibility of Electrical Impedance Tomography (EIT) to assess the interstitial fluid in subcutaneous adipose tissue as an enhancement method of bioelectrical impedance spectroscopy (BIS). Here, we demonstrate the preliminary result of EIT with a wearable 16 electrodes sensor. The image-based reference EIT with fat weighted threshold method is proposed. In order to evaluate the performance of our novel method, a physiological swelling experiment is conducted, and Multi-Frequency Bioelectrical Impedance Analysis (MFBIA) is also applied as a comparison with EIT results. The experimental results showed that the proposed method was able to distinguish the physiological swelling condition and effectively to remove the unexpected background noise. Furthermore, the conductivity variation in the subcutaneous layer had a good correlation with extracellular water volume change from MFBIA data; the correlation coefficient R2 = 0.927. It is concluded that the proposed method provides a significant prospect for SAT assessment.

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“…k is the number of iteration times in the Gauss-Newton iterative method supported by the EIDORS software (ver. 3.10) to solve (9) by iteration [28]. ∆Rs is normalized resistance as max r ii − = ss s s RR R R (10) where i is the measurement number, Rsi the measurement resistance of cell spheroids of ith measured pattern, Rst r is the reference resistance from the sucrose solution of ith measured pattern, and Rs max is the maximum resistance of all measured patterns.…”

Section: Image Reconstructionmentioning

confidence: 99%

Detection of Heterogeneous Cells in Cell Spheroids by Applying High-Frequency Second-Order Sensitivity Matrix Electrical Impedance Tomography (HSSM-EIT)

Kawashima

Gao

et al. 2022

IEEE Open J. Instrum. Meas.

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The High-frequency second-order sensitivity matrix electrical impedance tomography (HSSM-EIT) method has been proposed to detect heterogeneous cells in cell spheroids by coupling the high-frequency and second-order sensitivity matrix EIT. The sensitivity matrix with the first and second-order terms of Taylor's formula (Jacobian and Hessian) is applied to the image reconstruction of cell spheroids with the high-frequency injected current at 1 MHz, at which the impedance reflects intracellular contents to visualize the cytoplasm conductivity distribution of cell spheroids. The cell spheroids with five compositions percentages of wild type (WT) and green fluorescent protein type (GFPT) of MRC-5 human lung fibroblast cell line are 100/0%, 75/25%, 50/50%, 25/75%, and 0/100%, were cultured to mimic heterogeneous cells. As a result, the cell spheroid images reconstructed by HSSM-EIT clearly visualize the heterogeneity stage rather than the images reconstructed by general first-order sensitivity matrix EIT; moreover, the cytoplasm conductivity of cell spheroid is decreased with the increase of GFPT percentage. In order to confirm the cytoplasm conductivity reconstructed by HSSM-EIT, an equivalent circuit model containing a cell spheroid and extracellular fluid is employed to calculate the cytoplasm conductivity σcyto from the measurement of electrochemical impedance spectroscopy. The result shows that σcyto is also decreased with the increase of GFPT percentage, which shows the same trend as the cytoplasm conductivity reconstructed by HSSM-EIT.

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Electrical Impedance Tomography imaging using Gauss-Newton algorithm

Cited by 16 publications

References 10 publications

EIT for tactile sensing: considering artefacts in hyperparameter selection

EIT for tactile sensing: considering artefacts in hyperparameter selection

Investigation of physiological swelling on conductivity distribution in lower leg subcutaneous tissue by electrical impedance tomography

Detection of Heterogeneous Cells in Cell Spheroids by Applying High-Frequency Second-Order Sensitivity Matrix Electrical Impedance Tomography (HSSM-EIT)

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