Projects per year
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.
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 language | English |
---|---|
Journal | IEEE Transactions on Medical Imaging |
Early online date | 29 Mar 2018 |
DOIs | |
Publication status | E-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.
Fingerprint
Dive into the research topics of 'Image Reconstruction in Electrical Impedance Tomography Based on Structure-Aware Sparse Bayesian Learning'. Together they form a unique fingerprint.Projects
- 1 Finished
-
Cerebral Blood Flow Imaging based on 3D Electrical Impedance Tomography
1/02/17 → 31/05/18
Project: Research
Research output
- 1 Paper
-
Sequential EIT Frame Reconstruction Exploiting Spatiotemporal Correlation
Liu, S. & Jia, J., 2018.Research output: Contribution to conference › Paper › peer-review