scholarly journals Improving Image Quality in Electrical Impedance Tomography (EIT) Using Projection Error Propagation-Based Regularization (PEPR) Technique: A Simulation Study

2019 ◽  
Vol 2 (1) ◽  
pp. 2-12 ◽  
Author(s):  
Tushar Kanti Bera ◽  
Samir Kumar Biswas ◽  
K. Rajan ◽  
J. Nagaraju
Keyword(s):  
Image Quality ◽  
Electrical Impedance Tomography ◽  
Error Propagation ◽  
Electrical Impedance ◽  
Measured Data ◽  
Response Matrix ◽  
Reconstruction Accuracy ◽  
Impedance Tomography ◽  
Reconstruction Process ◽  
Projection Error

Abstract A Projection Error Propagation-based Regularization (PEPR) method is proposed and the reconstructed image quality is improved in Electrical Impedance Tomography (EIT). A projection error is produced due to the misfit of the calculated and measured data in the reconstruction process. The variation of the projection error is integrated with response matrix in each iteration and the reconstruction is carried out in EIDORS. The PEPR method is studied with the simulated boundary data for different inhomogeneity geometries. Simulated results demonstrate that the PEPR technique improves image reconstruction precision in EIDORS and hence it can be successfully implemented to increase the reconstruction accuracy in EIT.

scholarly journals An Effective Measured Data Preprocessing Method in Electrical Impedance Tomography

2014 ◽  
Vol 2014 ◽  
pp. 1-15 ◽  
Author(s):  
Chenglong Yu ◽  
Shihong Yue ◽  
Jianpei Wang ◽  
Huaxiang Wang
Keyword(s):  
Theoretical Model ◽  
Electrical Impedance Tomography ◽  
Electrical Impedance ◽  
Data Preprocessing ◽  
Measured Data ◽  
General Criterion ◽  
Impedance Tomography ◽  
Preprocessing Method ◽  
Detection Technology ◽  
Process Detection

As an advanced process detection technology, electrical impedance tomography (EIT) has widely been paid attention to and studied in the industrial fields. But the EIT techniques are greatly limited to the low spatial resolutions. This problem may result from the incorrect preprocessing of measuring data and lack of general criterion to evaluate different preprocessing processes. In this paper, an EIT data preprocessing method is proposed by all rooting measured data and evaluated by two constructed indexes based on all rooted EIT measured data. By finding the optimums of the two indexes, the proposed method can be applied to improve the EIT imaging spatial resolutions. In terms of a theoretical model, the optimal rooting times of the two indexes range in [0.23, 0.33] and in [0.22, 0.35], respectively. Moreover, these factors that affect the correctness of the proposed method are generally analyzed. The measuring data preprocessing is necessary and helpful for any imaging process. Thus, the proposed method can be generally and widely used in any imaging process. Experimental results validate the two proposed indexes.

scholarly journals Improving Conductivity Image Quality Using Block Matrix-based Multiple Regularization (BMMR) Technique in EIT: A Simulation Study

2019 ◽  
Vol 2 (1) ◽  
pp. 33-47 ◽  
Author(s):  
Tushar Kanti Bera ◽  
Samir Kumar Biswas ◽  
K. Rajan ◽  
J. Nagaraju
Keyword(s):  
Image Quality ◽  
Electrical Impedance ◽  
Regularization Parameter ◽  
Single Step ◽  
Block Matrix ◽  
Impedance Tomography ◽  
Conductivity Profile ◽  
Circular Domains ◽  
Conductivity Image ◽  
Projection Error

Abstract A Block Matrix based Multiple Regularization (BMMR) technique is proposed for improving conductivity image quality in Electrical Impedance Tomography (EIT). The response matrix (JTJ) has been partitioned into several sub-block matrices and the largest element of each sub-block matrix has been chosen as regularization parameter for the nodes contained by that sub-block. Simulated boundary data are generated for circular domains with circular inhomogeneities of different geometry and the conductivity images are reconstructed in a Model Based Iterative Image Reconstruction (MoBIIR) algorithm. Conductivity images are reconstructed with BMMR technique and the results are compared with the Single-step Tikhonov Regularization (STR) and modified Levenberg-Marquardt Regularization (LMR) methods. Results show that the BMMR technique improves the impedance image and its spatial resolution for single and multiple inhomogeneity phantoms of different geometries. It is observed that the BMMR technique reduces the projection error as well as the solution error and improves the conductivity reconstruction in EIT. Results also show that the BMMR method improves the image contrast and inhomogeneity conductivity profile by reducing background noise for all the phantom configurations.

scholarly journals Measurement-Based Domain Parameter Optimization in Electrical Impedance Tomography Imaging

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2507
Author(s):  
Jan Dusek ◽  
Jan Mikulka
Keyword(s):  
Image Reconstruction ◽  
Electrical Impedance Tomography ◽  
Electrical Impedance ◽  
Physical Models ◽  
Optimization Process ◽  
Shape Deformation ◽  
Impedance Tomography ◽  
Reconstruction Process ◽  
The Individual ◽  
The Impact

This paper discusses the optimization of domain parameters in electrical impedance tomography-based imaging. Precise image reconstruction requires accurate, well-correlated physical and numerical finite element method (FEM) models; thus, we employed the Nelder–Mead algorithm and a complete electrode model to evaluate the individual parameters, including the initial conductivity, electrode misplacement, and shape deformation. The optimization process was designed to calculate the parameters of the numerical model before the image reconstruction. The models were verified via simulation and experimental measurement with single source current patterns. The impact of the optimization on the above parameters was reflected in the applied image reconstruction process, where the conductivity error dropped by 6.16% and 11.58% in adjacent and opposite driving, respectively. In the shape deformation, the inhomogeneity area ratio increased by 11.0% and 48.9%; the imprecise placement of the 6th electrode was successfully optimized with adjacent driving; the conductivity error dropped by 12.69%; and the inhomogeneity localization exhibited a rise of 66.7%. The opposite driving option produces undesired duality resulting from the measurement pattern. The designed optimization process proved to be suitable for correlating the numerical and the physical models, and it also enabled us to eliminate imaging uncertainties and artifacts.

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