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 ◽  
...  

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.

2018 ◽  
Vol 41 (10) ◽  
pp. 2803-2815
Author(s):  
Qi Wang ◽  
Jing He ◽  
Jianming Wang ◽  
Xiuyan Li ◽  
Xiaojie Duan ◽  
...  

Electrical impedance tomography (EIT) is a new medical imaging technology that is used to estimate changes in the internal conductivity based on measurements of the border voltage disturbance. However, the generalized inverse operator of image reconstruction for EIT is ill-posed and ill-conditioned. In order to improve reconstruction quality, the structured sparse representation is integrated into the iterative process of the Symkaczmarz algorithm for EIT image reconstruction in this paper. The sparsity prior and the underlying structure characteristics of conductivity reconstruction are considered in the proposed algorithm. Both simulation and experiment results indicate that the proposed method has feasibility for pulmonary ventilation imaging and great potential for improving the image quality.


2019 ◽  
Vol 17 (9) ◽  
pp. 688-695
Author(s):  
Ramesh Kumar ◽  
Sharvan Kumar ◽  
A. Sengupta

This paper proposed an advanced digital voltage-controlled multi-frequency based constant current source, which is a wide range of loads and high SNR ratio for Electrical Impedance Tomography (EIT) application. In EIT a constant current source is required for injecting a sinusoidal current pulse to the phantom boundary. The boundary potentials are measured by inserting content current from the phantom boundary according to the variation in frequency and current levels. For studying the wide range of tissue conductivity among different type of subjects (the multi-frequency scanning) is desired in medical Electrical impedance tomography. The proposed Current source, which shows that the simulation has good performance at multi-frequency range with accuracy and stability. In proteus simulation software, the results show that the proposed circuit presents a more stable impedance output and the obtained boundary data at multi-frequency for the validation of the obtained data has been shown using suitable image reconstruction algorithm and is found suitable for image reconstruction much easier.


2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Jing Wang ◽  
Bo Han

The image reconstruction for electrical impedance tomography (EIT) mathematically is a typed nonlinear ill-posed inverse problem. In this paper, a novel iteration regularization scheme based on the homotopy perturbation technique, namely, homotopy perturbation inversion method, is applied to investigate the EIT image reconstruction problem. To verify the feasibility and effectiveness, simulations of image reconstruction have been performed in terms of considering different locations, sizes, and numbers of the inclusions, as well as robustness to data noise. Numerical results indicate that this method can overcome the numerical instability and is robust to data noise in the EIT image reconstruction. Moreover, compared with the classical Landweber iteration method, our approach improves the convergence rate. The results are promising.


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