Regularization Solver Guided FISTA for Electrical Impedance Tomography

dc.contributor.authorWang, Qian
dc.contributor.authorChen, Xiaoyan
dc.contributor.authorWang, Di
dc.contributor.authorWang, Zichen
dc.contributor.authorZhang, Xinyu
dc.contributor.authorXie, Na
dc.contributor.authorLiu, Lili
dc.contributor.otherTianjin University Science & Technology
dc.contributor.otherUniversity of Alabama Tuscaloosa
dc.date.accessioned2023-09-28T19:15:38Z
dc.date.available2023-09-28T19:15:38Z
dc.date.issued2023
dc.description.abstractElectrical impedance tomography (EIT) is non-destructive monitoring technology that can visualize the conductivity distribution in the observed area. The inverse problem for imaging is characterized by a serious nonlinear and ill-posed nature, which leads to the low spatial resolution of the reconstructions. The iterative algorithm is an effective method to deal with the imaging inverse problem. However, the existing iterative imaging methods have some drawbacks, such as random and subjective initial parameter setting, very time consuming in vast iterations and shape blurring with less high-order information, etc. To solve these problems, this paper proposes a novel fast convergent iteration method for solving the inverse problem and designs an initial guess method based on an adaptive regularization parameter adjustment. This method is named the Regularization Solver Guided Fast Iterative Shrinkage Threshold Algorithm (RS-FISTA). The iterative solution process under the L1-norm regular constraint is derived in the LASSO problem. Meanwhile, the Nesterov accelerator is introduced to accelerate the gradient optimization race in the ISTA method. In order to make the initial guess contain more prior information and be independent of subjective factors such as human experience, a new adaptive regularization weight coefficient selection method is introduced into the initial conjecture of the FISTA iteration as it contains more accurate prior information of the conductivity distribution. The RS-FISTA method is compared with the methods of Landweber, CG, NOSER, Newton-Raphson, ISTA and FISTA, six different distributions with their optimal parameters. The SSIM, RMSE and PSNR of RS-FISTA methods are 0.7253, 3.44 and 37.55, respectively. In the performance test of convergence, the evaluation metrics of this method are relatively stable at 30 iterations. This shows that the proposed method not only has better visualization, but also has fast convergence. It is verified that the RS-FISTA algorithm is the better algorithm for EIT reconstruction from both simulation and physical experiments.en_US
dc.format.mediumelectronic
dc.format.mimetypeapplication/pdf
dc.identifier.citationWang, Q., Chen, X., Wang, D., Wang, Z., Zhang, X., Xie, N., & Liu, L. (2023). Regularization Solver Guided FISTA for Electrical Impedance Tomography. In Sensors (Vol. 23, Issue 4, p. 2233). MDPI AG. https://doi.org/10.3390/s23042233
dc.identifier.doi10.3390/s23042233
dc.identifier.orcidhttps://orcid.org/0000-0002-4456-660X
dc.identifier.urihttps://ir.ua.edu/handle/123456789/11193
dc.languageEnglish
dc.language.isoen_US
dc.publisherMDPI
dc.rights.licenseAttribution 4.0 International (CC BY 4.0)
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectEIT
dc.subjectRS-FISTA
dc.subjectimage reconstruction
dc.subjectinverse problem
dc.subjectFISTA
dc.subjectIMAGE-RECONSTRUCTION ALGORITHM
dc.subjectELECTRODE MODELS
dc.subjectChemistry, Analytical
dc.subjectEngineering, Electrical & Electronic
dc.subjectInstruments & Instrumentation
dc.titleRegularization Solver Guided FISTA for Electrical Impedance Tomographyen_US
dc.typeArticle
dc.typetext
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