Survey on Medical Imaging of Electrical Impedance Tomography (EIT) by Variable Current Pattern Methods

2021 ◽  
Vol 2 (2) ◽  
pp. 82-95
Edriss Eisa Babikir Adam ◽  

Recently, the image reconstruction study on EIT plays a vital role in the medical application field for validation and calibration purpose. This research article analyzes the different types of reconstruction algorithms of EIT in medical imaging applications. Besides, it reviews many methods involved in constructing the electrical impedance tomography. The spatial distribution and resolution with different sensitivity has been discussed here. The electrode arrangement of various methods involved in the EIT system is discussed here. This research article comprises of adjacent drive method, cross method, and alternative opposite current direction method based on the voltage driven pattern. The assessment process of biomedical EIT has been discussed and investigated through the impedance imaging of the existent substances. The locality of the electrodes can be calculated and fixed for appropriate methods. More specifically, this research article discusses about the EIT image reconstruction methods and the significance of the alternative opposite current direction approach in the biomedical system. The change in conductivity test is further investigated based on the injection of current flow in the system. It has been established by the use of Electrical Impedance Tomography and Diffuse Optical Tomography Reconstruction Software (EDITORS) software, which is open-source software.

2018 ◽  
Vol 41 (10) ◽  
pp. 2803-2815
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.

2006 ◽  
Vol 48 ◽  
pp. 542-549 ◽  
Fang-Ming Yu ◽  
Chen-Ning Huang ◽  
Fang-Wei Chang ◽  
Hung-Yuan Chung

Sensor Review ◽  
2017 ◽  
Vol 37 (3) ◽  
pp. 257-269 ◽  
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.

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