Electrical Impedance Tomography (EIT) is an emerging imaging modality to monitor 3D cell culture dynamics through reconstructing the electrical properties of cell clusters. Recently, Machine Learning (ML) based approaches have achieved significant gains for the image reconstruction of EIT against conventional physical model-based methods. However, continuous, multi-level conductivity distributions, which commonly exists in cell culture imaging, are more rigorous to reconstruct and remains challenging. This paper aims to tackle this challenge by proposing a structure-aware dual-branch deep learning method to predict both structure distribution and conductivity values. The proposed network comprises two independent branches to encode respectively the structure and conductivity features. The two branches are jointed later to make final predictions of conductivity distributions. Numerical and experimental evaluation results demonstrate the superior performance of the proposed method in dealing with the multi-level, continuous conductivity reconstruction problem.
|Journal||IEEE Transactions on Instrumentation and Measurement|
|Publication status||Published - 25 Jun 2021|
- Deep Learning
- electrical impedance tomography (EIT)
- image reconstruction
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Edinburgh EIT Dataset
Guo, P. (Creator), Hu, D. (Creator), Wang, J. (Creator), Liu, Z. (Creator), Chen, Z. (Creator), Yang, Y. (Creator), Bagnaninchi, P. (Creator) & Lu, K. (Creator), Edinburgh DataShare, 26 May 2022
DOI: 10.7488/ds/3463, https://ieeexplore.ieee.org/abstract/document/9247286 and 3 more links, https://ieeexplore.ieee.org/abstract/document/9010151, https://ieeexplore.ieee.org/abstract/document/9128764, https://ieeexplore.ieee.org/abstract/document/9465170 (show fewer)