.png){kind=icon}
Return
Effective Electrical Impedance Tomography Based on Enhanced EncoderDecoder Using Atrous Spatial Pyramid Pooling Module
.png){kind=icon}
PRE
AI
DOI：10.1109/JBHI.2023.3265385.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Electrical impedance tomography (EIT) is a noninvasive and radiation-free imaging method. As a softfield imaging technique, in EIT, the target signal in the center of the measured field is frequently swamped by the target signal at the edge, which restricts its further application. To alleviate this problem, this study presents an enhanced encoder-decoder (EED) method with an atrous spatial pyramid pooling (ASPP) module. The proposed method enhances the ability to detect central weak targets by constructing an ASPP module that integrates multiscale information in the encoder. The multilevel semantic features are fused in the decoder to improve the boundary reconstruction accuracy of the center target. The average absolute error of the imaging results by the EED method reduced by 82.0%, 83.6%, and 36.5% in simulation experiments and 83.0%, 83.2%, and 36.1% in physical experiments compared with the errors of the damped least-squares algorithm, Kalman filtering method, and U-Net-based imaging method, respectively. The average structural similarity improved by 37.3%, 42.9%, and 3.6%, and 39.2%, 45.2%, and 3.8% in the simulation and physical experiments, respectively. The proposed method provides a practical and reliable means of extending the application of EIT by solving the problem of weak central target reconstruction under the effect of strong edge targets in EIT.
Keywords:
Deep learning
electrical impedance tomography
encoder-decoder
image reconstruction
Journal
IF:
6.8
Papers:
4.7K
Citations:
2.0W
Organization
Cited Papers
.png){kind=icon}
ShareSaveShareSaveA linkage of personal, food, and environmental hygiene to presence of E. coli in Warmindo Food Stall
OAAI
OAAIShareSaveShareSave
ShareSave
View more