Jia, Jiabin

Liu, Shengheng

Yang, Yunjie

Zhang, Yimin

English

Electrical impedance tomography (EIT) is developedto investigate the internal conductivity changes of anobject through a series of boundary electrodes, and has becomeincreasingly attractive in a broad spectrum of applications.However, the design of optimal tomography image reconstructionalgorithms has not achieved the adequate level of progress andmatureness. In this paper, we propose an efficient and highresolutionEIT image reconstruction method in the framework ofsparse Bayesian learning. Significant performance improvementis achieved by imposing structure-aware priors on the learningprocess to incorporate the prior knowledge that practical conductivitydistribution maps exhibit clustered sparsity and intraclustercontinuity. The proposed method not only achieves highresolutionestimation and preserves the shape information evenin low signal-to-noise ratio scenarios, but also avoids the timeconsumingparameter tuning process. The effectiveness of theproposed algorithm is validated through comparisons with stateof-the-art techniques using extensive numerical simulation andphantom experiment results

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

https://www.pure.ed.ac.uk/ws/files/57206562/SBL_EIT.pdf

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

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