A Physics-Embedded Dual-Learning Imaging Framework for Electrical Impedance Tomography
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866914200914231296 |
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| author | Yang, Xuanxuan Zhang, Yangming Chen, Haofeng Ma, Gang Wang, Xiaojie |
| author_facet | Yang, Xuanxuan Zhang, Yangming Chen, Haofeng Ma, Gang Wang, Xiaojie |
| contents | Electrical Impedance Tomography (EIT) is a promising noninvasive imaging technique that reconstructs the spatial conductivity distribution from boundary voltage measurements. However, it poses a highly nonlinear and ill-posed inverse problem. Traditional regularization-based methods are sensitive to noise and often produce significant artifacts. Physics-Embedded learning frameworks, particularly Physics-Informed Neural Networks (PINNs), have shown success in solving such inverse problems under ideal conditions with abundant internal data. Yet in practical EIT applications, only sparse and noisy boundary measurements are available. Moreover, changing boundary excitations require the simultaneous training of multiple forward networks and one inverse network, which significantly increases computational complexity and hampers convergence. To overcome these limitations, we propose a Physics-Embedded Dual-Learning Imaging Framework for EIT. The dual-learning strategy is composed of a supervised CNN-based forward network, which learns to predict a discrete internal potential distribution under fixed Neumann-to-Dirichlet boundary conditions, and an unsupervised PINN-based inverse network, which reconstructs the conductivity by enforcing the governing PDE through discrete numerical differentiation of the predicted potentials. This decoupled architecture removes the need for smooth conductivity assumptions, reduces the number of forward networks required from $K$ to 1, and improves reconstruction robustness and efficiency under realistic measurement constraints.(https://github.com/XuanxuanYang/CNN-PINNframework.git) |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_17827 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Physics-Embedded Dual-Learning Imaging Framework for Electrical Impedance Tomography Yang, Xuanxuan Zhang, Yangming Chen, Haofeng Ma, Gang Wang, Xiaojie Signal Processing Machine Learning Electrical Impedance Tomography (EIT) is a promising noninvasive imaging technique that reconstructs the spatial conductivity distribution from boundary voltage measurements. However, it poses a highly nonlinear and ill-posed inverse problem. Traditional regularization-based methods are sensitive to noise and often produce significant artifacts. Physics-Embedded learning frameworks, particularly Physics-Informed Neural Networks (PINNs), have shown success in solving such inverse problems under ideal conditions with abundant internal data. Yet in practical EIT applications, only sparse and noisy boundary measurements are available. Moreover, changing boundary excitations require the simultaneous training of multiple forward networks and one inverse network, which significantly increases computational complexity and hampers convergence. To overcome these limitations, we propose a Physics-Embedded Dual-Learning Imaging Framework for EIT. The dual-learning strategy is composed of a supervised CNN-based forward network, which learns to predict a discrete internal potential distribution under fixed Neumann-to-Dirichlet boundary conditions, and an unsupervised PINN-based inverse network, which reconstructs the conductivity by enforcing the governing PDE through discrete numerical differentiation of the predicted potentials. This decoupled architecture removes the need for smooth conductivity assumptions, reduces the number of forward networks required from $K$ to 1, and improves reconstruction robustness and efficiency under realistic measurement constraints.(https://github.com/XuanxuanYang/CNN-PINNframework.git) |
| title | A Physics-Embedded Dual-Learning Imaging Framework for Electrical Impedance Tomography |
| topic | Signal Processing Machine Learning |
| url | https://arxiv.org/abs/2412.17827 |