Abstract / Description of output
The intrinsic issue of low spatial resolution of Electrical Impedance Tomography (EIT) is a long-standing challenge that hinders the capability of performing quantitative analysis based on EIT image. Our recent work demonstrates an impedance-optical dual-modal imaging framework and a deep learning model named Multi-Scale Feature Cross Fusion Network (MSFCF-Net) to realize information fusion and high-quality EIT image reconstruction. However, this framework’s performance is limited by the accuracy of the mask image obtained from an auxiliary imaging modality. This paper further proposes a two-stage deep neural network, which is the enhanced version of the MSFCF-Net (named En-MSFCF-Net), to automatically improve mask image and conduct information fusion and image reconstruction. Compared to MSFCF-Net, En-MSFCF-Net demonstrates the superior ability to correct the inaccurate mask image, leading to a more accurate conductivity estimation. Furthermore, the En-MSFCF-Net also maintains the best shape preservation and conductivity prediction accuracy among given learning-based and model-based algorithms. Both qualitative and quantitative results indicate that En-MSFCF-Net could make dual-modal imaging more robust in real-world situations.
Original language | English |
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Pages (from-to) | 1 |
Number of pages | 1 |
Journal | IEEE Sensors Journal |
Early online date | 29 Aug 2022 |
DOIs | |
Publication status | E-pub ahead of print - 29 Aug 2022 |
Keywords / Materials (for Non-textual outputs)
- Conductivity
- deep learning
- Dual-modal imaging
- Electrical impedance tomography
- electrical impedance tomography
- Feature extraction
- image reconstruction
- Image reconstruction
- Imaging
- information fusion
- mask image correction
- Sensors
- Voltage