Image Reconstruction in Electrical Impedance Tomography Based on Structure-Aware Sparse Bayesian Learning

Shengheng Liu, Jiabin Jia, Yimin Zhang, Yunjie Yang

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Electrical impedance tomography (EIT) is developed
to investigate the internal conductivity changes of an
object through a series of boundary electrodes, and has become
increasingly attractive in a broad spectrum of applications.
However, the design of optimal tomography image reconstruction
algorithms has not achieved the adequate level of progress and
matureness. In this paper, we propose an efficient and highresolution
EIT image reconstruction method in the framework of
sparse Bayesian learning. Significant performance improvement
is achieved by imposing structure-aware priors on the learning
process to incorporate the prior knowledge that practical conductivity
distribution maps exhibit clustered sparsity and intracluster
continuity. The proposed method not only achieves highresolution
estimation and preserves the shape information even
in low signal-to-noise ratio scenarios, but also avoids the timeconsuming
parameter tuning process. The effectiveness of the
proposed algorithm is validated through comparisons with stateof-
the-art techniques using extensive numerical simulation and
phantom experiment results.
Original languageEnglish
JournalIEEE Transactions on Medical Imaging
Early online date29 Mar 2018
DOIs
Publication statusE-pub ahead of print - 29 Mar 2018

Keywords / Materials (for Non-textual outputs)

  • Inverse problem
  • electrical impedance tomography (EIT)
  • sparse Bayesian learning (SBL)
  • image reconstruction
  • maximum a posteriori (MAP) estimation.

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