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.

scholarly journals Reconstruction of conductivity change in lung lobes utilizing electrical impedance tomography

2017 ◽  
Vol 3 (2) ◽  
pp. 513-516 ◽  
Author(s):  
Benjamin Schullcke ◽  
Bo Gong ◽  
Sabine Krueger-Ziolek ◽  
Knut Moeller
Keyword(s):  
Image Reconstruction ◽  
Electrical Impedance Tomography ◽  
Lung Diseases ◽  
Electrical Impedance ◽  
Patient Specific ◽  
Impedance Tomography ◽  
Reconstruction Process ◽  
Obstructive Lung Diseases ◽  
Conductivity Change ◽  
Lung Lobes

AbstractElectrical Impedance Tomography (EIT) is a novel medical imaging technology which is expected to give valuable information for the treatment of mechanically ventilated patients as well as for patients with obstructive lung diseases. In lung-EIT electrodes are attached around the thorax to inject small alternating currents and to measure resulting voltages. These voltages depend on the internal conductivity distribution and thus on the amount of air in the lungs. Based on the measured voltages, image reconstruction algorithms are employed to generate tomographic images reflecting the regional ventilation of the lungs. However, the ill-posedness of the reconstruction problem leads to reconstructed images that are severely blurred compared to morphological imaging technologies, such as X-ray computed tomography or Magnetic Resonance Imaging. Thus, a correct identification of the particular ventilation in anatomically assignable units, e.g. lung-lobes, is often hindered. In this study a 3D-FEM model of a human thorax has been used to simulate electrode voltages at different lung conditions. Two electrode planes with 16 electrodes at each layer have been used and different amount of emphysema and mucus plugging was simulated with different severity in the lung lobes. Patient specific morphological information about the lung lobes is used in the image reconstruction process. It is shown that this kind of prior information leads to better reconstructions of the conductivity change in particular lung lobes than in classical image reconstruction approaches, where the anatomy of the patients’ lungs is not considered. Thus, the described approach has the potential to open new and promising applications for EIT. It might be used for diagnosis and disease monitoring for patients with obstructive lung diseases but also in other applications, e.g. during the placement of endobronchial valves in patients with severe emphysema.

A two-stage image reconstruction strategy for electrical impedance tomography

2021 ◽  
pp. 014233122110430
Author(s):  
Yanyan Shi ◽  
Xiaolong Kong ◽  
Meng Wang ◽  
Feng Fu ◽  
Yajun Lou
Keyword(s):  
Image Reconstruction ◽  
Electrical Impedance Tomography ◽  
Electrical Impedance ◽  
Superior Performance ◽  
Two Stage ◽  
Impedance Tomography ◽  
Conductivity Distribution ◽  
Alternating Minimization Algorithm ◽  
Reconstruction Strategy ◽  
The Impact

Electrical impedance tomography (EIT) is a potential and promising tomographic technique. Based on a reconstruction strategy, conductivity distribution can be imaged by processing boundary measurements. It should be noticed that the process of image reconstruction involves the solution of a nonlinear ill-posed inverse problem. To tackle this problem, a novel two-stage image reconstruction strategy is proposed in this work. It combines the advantages of total generalized variation regularization method and tight wavelet approach. The solution of the proposed method is acquired by employing alternating minimization algorithm and spilt Bregman algorithm. In the numerical simulation, reconstruction of five models is studied. Aside from the visual observation, we have also validated the proposed method with quantitative comparison. Meanwhile, the impact of noise on the reconstruction is considered. Furthermore, the proposed method is evaluated by phantom experimental data. The simulation and experimental results have demonstrated the superior performance of the proposed method in visualizing conductivity distribution.

Patch-based sparse reconstruction for electrical impedance tomography

Sensor Review ◽  
2017 ◽  
Vol 37 (3) ◽  
pp. 257-269 ◽  
Author(s):  
Qi Wang ◽  
Pengcheng Zhang ◽  
Jianming Wang ◽  
Qingliang Chen ◽  
Zhijie Lian ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Sparse Representation ◽  
Electrical Impedance Tomography ◽  
Electrical Impedance ◽  
Inverse Operator ◽  
Reconstruction Algorithm ◽  
Impedance Tomography ◽  
Content Type ◽  
Ill Posed ◽  
High Level

Purpose Electrical impedance tomography (EIT) is a technique for reconstructing the conductivity distribution by injecting currents at the boundary of a subject and measuring the resulting changes in voltage. Image reconstruction for EIT is a nonlinear problem. A generalized inverse operator is usually ill-posed and ill-conditioned. Therefore, the solutions for EIT are not unique and highly sensitive to the measurement noise. Design/methodology/approach This paper develops a novel image reconstruction algorithm for EIT based on patch-based sparse representation. The sparsifying dictionary optimization and image reconstruction are performed alternately. Two patch-based sparsity, namely, square-patch sparsity and column-patch sparsity, are discussed and compared with the global sparsity. Findings Both simulation and experimental results indicate that the patch based sparsity method can improve the quality of image reconstruction and tolerate a relatively high level of noise in the measured voltages. Originality/value EIT image is reconstructed based on patch-based sparse representation. Square-patch sparsity and column-patch sparsity are proposed and compared. Sparse dictionary optimization and image reconstruction are performed alternately. The new method tolerates a relatively high level of noise in measured voltages.

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