| Abstrakt: |
Electrical impedance tomography (EIT) is a technique to obtain conductivity maps from electrical voltage measurements in a region of interest. In this work, we discuss the proposal we submitted to the Kuopio Tomography Challenge 2023, whose aim was to reconstruct and segment EIT images obtained from limited data, after electrode disconnection. Our proposal, denoted 01A, consisted of an initial reconstruction using the smoothness prior and post-processing steps, including denoising and deblurring with a convolutional neural network (CNN), as a way to integrate deep learning and inverse problems. The score was calculated using the structural similarity index in 21 test cases. While the score of the reconstruction using only smoothness prior was 9.69, the score of 01A was 12.75. Also, we developed an improved proposal, denoted 01A+, using hyperparameter optimization and its score was 13.30. We obtained better results using 01A and 01A+ than using the original smoothness prior, but the proposals lacked consistency when more electrodes were disconnected and when the targets were too different from the CNN training data. Even so, 01A obtained second place at KTC2023, representing a way to remove artifacts from electrode disconnection in EIT reconstructions. [ABSTRACT FROM AUTHOR] |
