The inverse electrical impedance tomography (EIT) problem involves collecting electrical measurements on the smooth boundary of a region to determine the spatially varying electrical conductivity distribution within the bounded region. Effective applications of EIT technology emerged in different areas of engineering, technology, and applied sciences. However, the mathematical formulation of EIT is well known to suffer from a high degree of nonlinearity and severe ill-posedness. Therefore, regularization is required to produce reasonable electrical impedance images. Using difference imaging, we propose a spatially-variant variational method which couples sparsity regularization and smoothness regularization for improved EIT linear reconstructions. The EIT variational model can benefit from structural prior information in the form of an edge detection map coming either from an auxiliary image of the same object being reconstructed or automatically detected. We propose an efficient algorithm for minimizing the (non-convex) function based on the alternating direction method of multipliers. Experiments are presented which strongly indicate that using non-convex versus convex variational EIT models holds the potential for more accurate reconstructions.

Huska M., Lazzaro D., Morigi S., Samore A., Scrivanti G. (2020). Spatially-Adaptive Variational Reconstructions for Linear Inverse Electrical Impedance Tomography. JOURNAL OF SCIENTIFIC COMPUTING, 84(3), 1-29 [10.1007/s10915-020-01295-w].

Spatially-Adaptive Variational Reconstructions for Linear Inverse Electrical Impedance Tomography

Huska M.
Membro del Collaboration Group
;
Lazzaro D.
Membro del Collaboration Group
;
Morigi S.
Membro del Collaboration Group
;
Scrivanti G.
Membro del Collaboration Group
2020

Abstract

The inverse electrical impedance tomography (EIT) problem involves collecting electrical measurements on the smooth boundary of a region to determine the spatially varying electrical conductivity distribution within the bounded region. Effective applications of EIT technology emerged in different areas of engineering, technology, and applied sciences. However, the mathematical formulation of EIT is well known to suffer from a high degree of nonlinearity and severe ill-posedness. Therefore, regularization is required to produce reasonable electrical impedance images. Using difference imaging, we propose a spatially-variant variational method which couples sparsity regularization and smoothness regularization for improved EIT linear reconstructions. The EIT variational model can benefit from structural prior information in the form of an edge detection map coming either from an auxiliary image of the same object being reconstructed or automatically detected. We propose an efficient algorithm for minimizing the (non-convex) function based on the alternating direction method of multipliers. Experiments are presented which strongly indicate that using non-convex versus convex variational EIT models holds the potential for more accurate reconstructions.
2020
Huska M., Lazzaro D., Morigi S., Samore A., Scrivanti G. (2020). Spatially-Adaptive Variational Reconstructions for Linear Inverse Electrical Impedance Tomography. JOURNAL OF SCIENTIFIC COMPUTING, 84(3), 1-29 [10.1007/s10915-020-01295-w].
Huska M.; Lazzaro D.; Morigi S.; Samore A.; Scrivanti G.
File in questo prodotto:
File Dimensione Formato  
s10915-020-01295-w.pdf

accesso aperto

Tipo: Versione (PDF) editoriale
Licenza: Licenza per Accesso Aperto. Creative Commons Attribuzione (CCBY)
Dimensione 4.33 MB
Formato Adobe PDF
4.33 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/773393
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 20
  • ???jsp.display-item.citation.isi??? 19
social impact