Semantic contrastive learning for orthogonal X-ray computed tomography reconstruction
This work addresses medical imaging challenges for reducing radiation dose in CT scans, but it is incremental as it builds on existing deep learning methods.
The paper tackles the problem of streak artifacts in sparse-view X-ray CT reconstruction by proposing a semantic contrastive learning loss function, achieving superior reconstruction quality and faster processing on a chest dataset with orthogonal projections.
X-ray computed tomography (CT) is widely used in medical imaging, with sparse-view reconstruction offering an effective way to reduce radiation dose. However, ill-posed conditions often result in severe streak artifacts. Recent advances in deep learning-based methods have improved reconstruction quality, but challenges still remain. To address these challenges, we propose a novel semantic feature contrastive learning loss function that evaluates semantic similarity in high-level latent spaces and anatomical similarity in shallow latent spaces. Our approach utilizes a three-stage U-Net-based architecture: one for coarse reconstruction, one for detail refinement, and one for semantic similarity measurement. Tests on a chest dataset with orthogonal projections demonstrate that our method achieves superior reconstruction quality and faster processing compared to other algorithms. The results show significant improvements in image quality while maintaining low computational complexity, making it a practical solution for orthogonal CT reconstruction.