Abstract
The problem of efficient, high-resolution 3-D image reconstruction from multi-frequency Electrical Impedance Tomography (EIT) data sequences has attracted significant attention from researchers due to its practical importance in process analysis of chemical or biomedical engineering. To tackle this challenge, we propose in this paper a novel image reconstruction algorithm exploiting the spatial-frequency correlations of the image series, in order to perform efficient 3-D image reconstructions using multi-frequency EIT data. The main contribution of this paper includes the development of an extended joint sparsity framework. This combines the structural characters of time-difference conductivity distribution and the structural correlations among frequency-difference images. In addition, a dynamic 3-D structural feature extraction method was developed to iteratively group the voxels with similarities. The Alternating Direction Method of Multipliers framework was employed to solve the inversion problem. Phantom experiments were conducted to verify the performance of this new method. The results suggest that the algorithm proposed is fast, stable and yields superior reconstructions when compared with other state-of-the-art algorithms.
Original language | English |
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Title of host publication | PROCEEDINGS OF THE 9TH WORLD CONGRESS ON INDUSTRIAL PROCESS TOMOGRAPHY |
Publisher | International Society for Industrial Process Tomography |
Pages | 641-650 |
ISBN (Electronic) | 978 0 85316 3497 |
ISBN (Print) | 978 0 853 16356 5 |
Publication status | Published - Sept 2018 |