Dual-Energy Computed Tomography reconstruction of multi-material objects via a Swin Transformer architecture

This paper introduces a Dual-Energy Swin U-Network architecture (DE-Swin U-Net), applied to Dual-Energy Computed Tomography (DECT) scans, to mitigate image artefacts generated during the tomographic inspection of multi-material assemblies or components. Recently, vision transformers have been replacing more traditional convolutional neural network architectures in the field of computer vision, due to their ability to model long-range relationships and global context via self-attention. Considering this, the authors propose a model architecture consisting of an encoder-decoder framework: the encoder, based on a Swin Transformer, extracts features and patterns from the collected dual-energy 2D projections, while the decoder reconstructs the final image from these features. The model is trained via a custom data generation pipeline, where simulated CT projections, at two energy levels, are generated from CAD models of electrical components. In addition, an ideal monochromatic reconstruction is also produced, serving as the ground truth. The proposed method demonstrates preliminary reconstruction performance of DECT scans, particularly with respect to reconstruction speed and the structural similarity index measure.