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

• Published in: IEEE transactions on medical imaging Vol. 37; no. 9; pp. 2090 - 2102
• Main Authors: Liu, Shengheng, Jia, Jiabin, Zhang, Yimin D., Yang, Yunjie
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.09.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Bayes methods; Bayes Theorem; Bayesian analysis; Computational modeling; Computer Simulation; Conductivity; Electric Impedance; Electrical impedance; electrical impedance tomography (EIT); Electrical resistivity; Electrodes; High resolution; Image processing; Image Processing, Computer-Assisted - methods; Image reconstruction; Image resolution; Impedance; Inverse problem; Learning; Machine learning; Mathematical models; maximum <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">a posteriori (MAP) estimation; Medical imaging; Phantoms, Imaging; Process parameters; Signal processing; sparse Bayesian learning (SBL); Tomography; Tomography - methods; Voltage measurement
• ISSN: 0278-0062; 1558-254X; 1558-254X
• DOI: 10.1109/TMI.2018.2816739

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 high-resolution 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 intra-cluster continuity. The proposed method not only achieves high-resolution estimation and preserves the shape information even in low signal-to-noise ratio scenarios but also avoids the time-consuming parameter tuning process. The effectiveness of the proposed algorithm is validated through comparisons with state-of-the-art techniques using extensive numerical simulation and phantom experiment results.