Dual-modal Image Reconstruction for Electrical Impedance Tomography with Overlapping Group Lasso and Laplacian Regularization

Zhe Liu, Hengjia Gu, Zhou Chen, Pierre Olivier Bagnaninchi, Yunjie Yang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Objective: Electrical Impedance Tomography (EIT) is a promising biomedical imaging modality, yet EIT image reconstruction remains an open challenge due to its severe ill-posedness. High-quality EIT image reconstruction algorithms are desired. Methods: This paper reports a segmentation-free dual-modal EIT image reconstruction algorithm that uses Overlapping Group Lasso and Laplacian (OGLL) regularization. An overlapping group lasso penalty is constructed based on conductivity change properties and encodes the imaging targets’ structural information obtained from an auxiliary imaging modality that provides structural images of the sensing region. We introduce Laplacian regularization to alleviate the artifacts caused by group overlapping. Results: The performance of OGLL is evaluated and compared with single-modal and dual-modal image reconstruction algorithms using simulation and real-world data. Quantitative metrics and visualized images confirm the superiority of the proposed method in terms of structure preservation, background artifact (BA) suppression, and conductivity contrast differentiation. Conclusion: This work proves the effectiveness of OGLL in improving EIT image quality. Significance: This study demonstrates that EIT has the potential to be adopted in quantitative tissue analysis by using such dual-modal imaging approaches.
Original languageEnglish
Pages (from-to)2362-2373
JournalIEEE Transactions on Biomedical Engineering
Volume70
Issue number8
Early online date9 Feb 2023
DOIs
Publication statusPublished - 1 Aug 2023

Keywords / Materials (for Non-textual outputs)

  • Conductivity
  • Dual-modal imaging
  • Electrical Impedance Tomography
  • Electrical impedance tomography
  • Image quality
  • Image reconstruction
  • Imaging
  • Inverse problems
  • Laplace equations
  • Laplacian regularization
  • image reconstruction
  • overlapping group lasso

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