Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.14521 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913801038725120 |
|---|---|
| author | Song, Zhenzhong Li, Jianping Zhang, Jun Wen, Hanyun Zhang, Suqin Jiang, Wei Zhou, Xingxing |
| author_facet | Song, Zhenzhong Li, Jianping Zhang, Jun Wen, Hanyun Zhang, Suqin Jiang, Wei Zhou, Xingxing |
| contents | Electrical impedance tomography (EIT) is a non-invasive functional imaging technology. In order to enhance the quality of lung EIT images, novel algorithms, namely LSTM-LSTM, LSTM-BiLSTM, BiLSTM-LSTM, and BiLSTM-BiLSTM, leveraging LSTM or BiLSTM networks, were developed. Simulation results demonstrate that the optimized deep recurrent neural network significantly enhanced the quality of the reconstructed images. Specifically, the correlation coefficients of the LSTM-LSTM and the LSTM-BiLSTM algorithms exhibited maximum increases of 27.5% and 25.4% over the LSTM algorithm, respectively. Moreover, in comparison to the BiLSTM algorithm, the correlation coefficients of the BiLSTM-LSTM and BiLSTM-BiLSTM algorithms increased by 11.7% and 13.4%, respectively. Overall, the quality of EIT images showed notable enhancement. This research offers a valuable approach for enhancing EIT image quality and presents a novel application of LSTM networks in EIT technology. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_14521 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Pulmonary electrical impedance tomography based on deep recurrent neural networks Song, Zhenzhong Li, Jianping Zhang, Jun Wen, Hanyun Zhang, Suqin Jiang, Wei Zhou, Xingxing Biological Physics Electrical impedance tomography (EIT) is a non-invasive functional imaging technology. In order to enhance the quality of lung EIT images, novel algorithms, namely LSTM-LSTM, LSTM-BiLSTM, BiLSTM-LSTM, and BiLSTM-BiLSTM, leveraging LSTM or BiLSTM networks, were developed. Simulation results demonstrate that the optimized deep recurrent neural network significantly enhanced the quality of the reconstructed images. Specifically, the correlation coefficients of the LSTM-LSTM and the LSTM-BiLSTM algorithms exhibited maximum increases of 27.5% and 25.4% over the LSTM algorithm, respectively. Moreover, in comparison to the BiLSTM algorithm, the correlation coefficients of the BiLSTM-LSTM and BiLSTM-BiLSTM algorithms increased by 11.7% and 13.4%, respectively. Overall, the quality of EIT images showed notable enhancement. This research offers a valuable approach for enhancing EIT image quality and presents a novel application of LSTM networks in EIT technology. |
| title | Pulmonary electrical impedance tomography based on deep recurrent neural networks |
| topic | Biological Physics |
| url | https://arxiv.org/abs/2504.14521 |