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| Autores principales: | , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2505.10731 |
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| _version_ | 1866908366809333760 |
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| author | Song, Zhenzhong Li, Jianping Yao, Jiafeng Wang, Linying Zhu, Dan Zhang, Lvjun Wen, Jianming Wan, Nen Ma, Jijie Zhang, Yu Gao, Zengfeng |
| author_facet | Song, Zhenzhong Li, Jianping Yao, Jiafeng Wang, Linying Zhu, Dan Zhang, Lvjun Wen, Jianming Wan, Nen Ma, Jijie Zhang, Yu Gao, Zengfeng |
| contents | Electrical impedance tomography (EIT) is a novel computational imaging technology. In order to improve the quality and spatial resolution of the reconstructed images, the G-CNN and HG-CNN algorithms are proposed based on a one-dimensional convolutional neural network (1D-CNN) in this paper. The input of the 1D-CNN is the reconstructed conductivity distribution obtained by the GVSPM algorithm or the H-GVSPM algorithm. The reconstructed images with higher resolution are obtained through the calculation of 1D-CNN. Finally, the Hadamard product is applied to calculate the output of the 1D-CNN. In the simulation results of the lung cross-section models, the correlation coefficients of the G-CNN algorithm and HG-CNN algorithm maximumly are 2.52 times and 2.20 times greater than the GVSPM algorithm and H-GVSPM algorithm, respectively. In the results of the simulation and experiment, the reconstructed images of the G-CNN and HG-CNN algorithms are distortion-free. In addition, the artifacts of the reconstructed images are diminished after calculations of the Hadamard product. This research provides a reference method for improving the quality of the reconstructed images so that EIT is better applied in medical detection. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_10731 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A reconstruction algorithm of electrical impedance tomography based on one-dimensional convolutional neural network Song, Zhenzhong Li, Jianping Yao, Jiafeng Wang, Linying Zhu, Dan Zhang, Lvjun Wen, Jianming Wan, Nen Ma, Jijie Zhang, Yu Gao, Zengfeng Medical Physics Electrical impedance tomography (EIT) is a novel computational imaging technology. In order to improve the quality and spatial resolution of the reconstructed images, the G-CNN and HG-CNN algorithms are proposed based on a one-dimensional convolutional neural network (1D-CNN) in this paper. The input of the 1D-CNN is the reconstructed conductivity distribution obtained by the GVSPM algorithm or the H-GVSPM algorithm. The reconstructed images with higher resolution are obtained through the calculation of 1D-CNN. Finally, the Hadamard product is applied to calculate the output of the 1D-CNN. In the simulation results of the lung cross-section models, the correlation coefficients of the G-CNN algorithm and HG-CNN algorithm maximumly are 2.52 times and 2.20 times greater than the GVSPM algorithm and H-GVSPM algorithm, respectively. In the results of the simulation and experiment, the reconstructed images of the G-CNN and HG-CNN algorithms are distortion-free. In addition, the artifacts of the reconstructed images are diminished after calculations of the Hadamard product. This research provides a reference method for improving the quality of the reconstructed images so that EIT is better applied in medical detection. |
| title | A reconstruction algorithm of electrical impedance tomography based on one-dimensional convolutional neural network |
| topic | Medical Physics |
| url | https://arxiv.org/abs/2505.10731 |