Applicability of electrical resistance tomography to the analysis of fluid distribution in haemodialysis modules

Paglianti, Alessandro; Marotta, Gaspare; Montante, Giuseppina Maria Rosa

doi:10.1002/cjce.23826

Cited by 6 publications

(4 citation statements)

References 23 publications

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“…Applicability of ERT has also been explored for the analysis of fluid distribution in haemodialysis modules, an intricate process due to the complex geometry of the hollow fiber bundles and small sizes of the modules. Here, reliable conductivity maps are obtained by placing the module vertically or horizontally [18]. Experimental data collected for liquid mixtures of different sodium chloride concentrations have further shown that the ERT technique is suitable for detecting concentration variations.…”

Section: Introductionmentioning

confidence: 99%

Hydromechanics-based flow velocity estimation using single ERT sensor

et al. 2022

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One of the major tasks in multi-phase ﬂow detection is flow velocity estimation. Owing to its fast response, no radiation and low cost, the electrical resistance tomography (ERT) technique with a pair of sensors can estimate the flow velocity according to the cross-correlation (CC) measurement principle. However, due to the unreasonable a priori assumption, uncertain parameter, and limitation of ERT, this class of techniques remain inaccurate and unsteady under complex and multi-flow conditions. In this paper, we propose a novel flow velocity computation method by using a single ERT sensor instead of a pair. Different from the existing methods that are based on the CC principle, both the Churchill and Bernoulli equations in hydromechanics are used to compute the flowing velocity, based on which an inherent relation among the flow velocity, pressure difference and density value in a detected pipeline is derived. Since the pressure difference and density value can be accurately computed based on the measurements by a single ERT sensor, respectively, the estimated flow velocity can greatly overcome the limitations when using a pair of ERT sensors. The results of a group of typical experiments validate the proposed method, showing that it is more accurate and steadier.

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Section: Introductionmentioning

confidence: 99%

Hydromechanics-based flow velocity estimation using single ERT sensor

et al. 2022

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“…Based on the different electrical features of the object in the sensitivity field, the ET techniques could be divided into electromagnetic tomography (EMT), electrical capacitance tomography (ECT), electrical impedance tomography (EIT), and electrical resistance tomography (ERT). Based on the principle of electromagnetic induction, the EMT can reconstruct the distribution state of the magnetic permeability for the medium in the sensitivity field [1][2][3][4][5][6]; Based on the principle of capacitance sensitivity, the ECT can reconstruct the distribution state of the dielectric constant for the medium in the sensitivity field [7][8][9][10][11][12][13][14][15][16][17]; Based on the principle of impedance sensitivity, the EIT can reconstruct the distribution state of the complex admittance for the medium in the sensitivity field [18][19][20][21][22][23][24]; Based on the principle of resistance sensing, the ERT can reconstruct the distribution state of the dielectric resistivity/conductivity for the medium in the sensitivity field [25][26][27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Design and Optimization of a Finite Element Model for Electrical Resistance Tomography of Human Lungs

Xiao¹

2021

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To improve the reconstruction quality of electrical resistance tomography (ERT) images, this paper designs and optimizes the finite element model for human lungs. According to the computer tomography (CT) scan image on human chest, the entire simulation domain was divided into a region of lungs, heart, and spine, and a region of adipose tissue, and the boundary curve equations of each region were derived by the improved particle swarm optimization (PSO). Based on the prior knowledge, a structural model was established for human lungs; the ERT forward problem was solved by the finite element model based on grid reconstruction, and the calculated boundary voltage of sensitivity field was taken as the theoretical value. Next, two optimization goals were set up: improving the calculation accuracy of forward problem, and easing the ill-conditionedness of the sensitivity matrix; two variables were configured: the number of layers of the finite element model in each region, and the polar diameter ratio of the finite element nodes on each layer to the finite element nodes on the boundary of each region corresponding to the same polar angle. On this basis, the finite element model was optimized by the improved PSO to adapt to human lung ERT. Simulation results show that, under the same experimental conditions, the proposed finite element model could solve the forward problem more accurately, improve the ill-conditionedness of the sensitivity matrix and the Hessian matrix, and make the sensitivity distribution more uniformly, thereby enhancing the accuracy of image reconstruction.

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“…Electrical tomography (ET) technology consists of 4 different branches, namely, electrical impedance tomography (EIT) [8][9][10][11][12][13][14], ERT [15][16][17][18][19][20], electrical capacitance tomography (ECT) [21][22][23][24][25][26][27], and electromagnetic tomography (EMT) [28][29][30][31][32][33], among which the ERT technology is a new generation of medical imaging technology and a simplified form of the EIT technology when only the change of conductivity/resistivity of the sensitive field is considered, having three outstanding advantages of functional imaging, no damage, and medical image monitoring. Compared with other monitoring methods, ERT technology has incomparable advantages in real-time monitoring and quantitative evaluation of critical respiratory diseases.…”

Section: Introductionmentioning

confidence: 99%

Width Optimization of Array Electrode for Human Lung Electrical Resistance Tomography System Based on prior Knowledge

Xiao

2021

Complexity

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In electrical resistance tomography (ERT) technology for human lung, under the same experimental conditions, the width of the sensitive field boundary electrode has a significant impact on the calculation accuracy of the inverse problem besides the finite element model (FEM) topology. Aiming to improve the quality of reconstructed images, the FEM for human lung was set up based on prior knowledge. On this basis, the electrode width of the FEM was optimised by comparing the morbidity degrees of the sensitivity matrix and Hessian matrix, the uniformity of sensitivity distribution, and the quality of reconstructed images, which can improve the accuracy of solving the inverse problem significantly.

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Applicability of electrical resistance tomography to the analysis of fluid distribution in haemodialysis modules

Cited by 6 publications

References 23 publications

Hydromechanics-based flow velocity estimation using single ERT sensor

Hydromechanics-based flow velocity estimation using single ERT sensor

Design and Optimization of a Finite Element Model for Electrical Resistance Tomography of Human Lungs

Width Optimization of Array Electrode for Human Lung Electrical Resistance Tomography System Based on prior Knowledge

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