scholarly journals An analysis of electrical impedance tomography with applications to Tikhonov regularization

2012 ◽  
Vol 18 (4) ◽  
pp. 1027-1048 ◽  
Author(s):  
Bangti Jin ◽  
Peter Maass
Keyword(s):  
Electrical Impedance Tomography ◽  
Tikhonov Regularization ◽  
Electrical Impedance ◽  
Impedance Tomography

Tikhonov regularization and prior information in electrical impedance tomography

1998 ◽  
Vol 17 (2) ◽  
pp. 285-293 ◽  
Author(s):  
M. Vauhkonen ◽  
D. Vadasz ◽  
P.A. Karjalainen ◽  
E. Somersalo ◽  
J.P. Kaipio
Keyword(s):  
Electrical Impedance Tomography ◽  
Tikhonov Regularization ◽  
Electrical Impedance ◽  
Prior Information ◽  
Impedance Tomography

A COMBINED REGULARIZATION ALGORITHM FOR ELECTRICAL IMPEDANCE TOMOGRAPHY SYSTEM USING RECTANGULAR ELECTRODES ARRAY

2012 ◽  
Vol 24 (04) ◽  
pp. 313-322 ◽  
Author(s):  
Wei He ◽  
Bing Li ◽  
Zheng Xu ◽  
Haijun Luo ◽  
Peng Ran
Keyword(s):  
Inverse Problem ◽  
Electrical Impedance Tomography ◽  
Tikhonov Regularization ◽  
Electrical Impedance ◽  
Regularization Parameter ◽  
Impedance Tomography ◽  
Regularization Algorithm ◽  
Tomography System ◽  
One Step ◽  
Mean Square Distance

A novel Electrical Impedance Tomography system with rectangular electrodes array and back electrode is proposed. This system could reconstruct a deeper target and is easy to operate. By studying different reconstructed algorithms: Tikhonov regularization and the Newton's One-step Error Reconstructor (NOSER), a combined regularization algorithm is proposed. The L-curve and posteriori method are used to choose Tikhonov and NOSER regularization parameter. Two evaluation parameters of reconstructed algorithm: normalization mean square distance criterion (NMSD), normalized mean absolute distance criterion (NMAD) are used to evaluate the result's precision of inverse problem quantificationally. The comparison among Tikhonov regularization, NOSER and the combined regularization shows that the ill-condition and the error of inverse problem are reduced. This new algorithm can decrease condition number by 70%, NMSD by 51%, and NMAD by 41% at least. Simulate results show that the combined regularization algorithm could reconstructed the target image in the depth from 10–40 mm. The experimental results show that a 15 mm × 9 mm × 9 mm cuboids whose depth is 35 mm could be distinguished. The performance of this system and the combined regularization algorithm demonstrate significantly better spatial resolution and minor reconstructed error.

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