Self-Attention-Powered Graph Convolution Network for Image Reconstruction in ERT/UTT Dual-Modality Tomography

A self-attention powered graph convolution network (GCN) is proposed for electrical resistance tomography (ERT) and ultrasonic transmission tomography (UTT) dual-modality tomography. It’s promising to improve the imaging accuracy in cross-sectional media distribution detection. The proposed model uses polynomial feature generation to pre-process the two input layers of measurement data. The dual-branch measurement spatial information is fused by the feature level at the dense layer with the same dimension. The proposed model calculates attention scores to extract contrasts from complex non-Euclidean distance measurement data structures. The GCN with an attention mechanism (GCNAM) identifies expressive complementary features from the combined ERT/UTT measurement data, improving prediction accuracy. During training, the attention mechanism predicts the various modal features using learnable parameters; the model then adapts to the good reconstruction target. It promotes the effective fusion and image reconstruction of dual-modal information between different modal features. The optimizer RMSprop further fine-tunes the proposed model to enhance the generalization ability. The simulation and experiment tests are carried out to evaluate the proposed model’s performance. Furthermore, it conducts generalization and robustness tests on all state-of-the-art models to evaluate their performance under varying conditions. The results demonstrate that the proposed model maintains higher imaging accuracy and stability using structural similarity (SSIM) index and root mean square error (RMSE) metrics.