2019/01/09 by Zhun Wei, Dong Liu, Xudong Chen · 174 citations
Engineering · Mathematics · #Algorithm #Artificial intelligence #Computer science #Convolutional neural network #Deep learning #Electrical and Bioimpedance Tomography #Electrical impedance tomography #Imaging phantom #Inverse problem #Iterative method #Iterative reconstruction #Mathematics #Microfluidic and Bio-sensing Technologies #Microwave Imaging and Scattering Analysis #Optics #Pattern recognition (psychology) #Physics #Regularization (linguistics) #Subspace topology #Tomography
paper · doi:10.1109/tbme.2019.2891676
published in IEEE Transactions on Biomedical Engineering 66(9), 2546-2555 (Institute of Electrical and Electronics Engineers)
openalex publication_date 2019/01/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29
OBJECTIVE: Deep learning has recently been applied to electrical impedance tomography (EIT) imaging. Nevertheless, there are still many challenges that this approach has to face, e.g., targets with sharp corners or edges cannot be well recovered when using circular inclusion training data. This paper proposes an iterative-based inversion method and a convolutional neural network (CNN) based inversion method to recover some challenging inclusions such as triangular, rectangular, or lung shapes, where the CNN-based method uses only random circle or ellipse training data. METHODS: First, the iterative method, i.e., bases-expansion subspace optimization method (BE-SOM), is proposed based on a concept of induced contrast current (ICC) with total variation regularization. Second, the theoretical analysis of BE-SOM and the physical concepts introduced there motivate us to propose a dominant-current deep learning scheme for EIT imaging, in which dominant parts of ICC are utilized to generate multi-channel inputs of CNN. RESULTS: The proposed methods are tested with both numerical and experimental data, where several realistic phantoms including simulated pneumothorax and pleural effusion pathologies are also considered. CONCLUSIONS AND SIGNIFICANCE: Significant performance improvements of the proposed methods are shown in reconstructing targets with sharp corners or edges. It is also demonstrated that the proposed methods are capable of fast, stable, and high-quality EIT imaging, which is promising in providing quantitative images for potential clinical applications.