Semantic Contrastive Learning for CT Reconstruction

This paper addresses the challenge of improving X-ray Computed Tomography (CT) reconstruction, particularly for sparse-view scenarios, which are crucial for reducing radiation dose. The core contribution is a novel semantic feature contrastive learning loss function designed to enhance image quality by evaluating semantic and anatomical similarities across different latent spaces within a U-Net-based architecture. The paper's significance lies in its potential to improve medical imaging quality while minimizing radiation exposure and maintaining computational efficiency, making it a practical advancement in the field.

• •Proposes a novel semantic feature contrastive learning loss function for CT reconstruction.
• •Employs a three-stage U-Net-based architecture.
• •Demonstrates superior reconstruction quality and faster processing compared to existing methods.
• •Focuses on orthogonal CT reconstruction, relevant for reducing radiation dose.