Semantic contrastive learning for orthogonal X-ray computed tomography reconstruction

TL;DR

This paper introduces a semantic contrastive learning approach for orthogonal X-ray CT reconstruction, improving image quality and processing speed in sparse-view scenarios using a three-stage U-Net architecture.

Contribution

It proposes a novel semantic feature contrastive loss and a three-stage U-Net framework for enhanced orthogonal CT reconstruction, addressing artifacts and efficiency.

Findings

Achieves superior reconstruction quality on chest datasets.

Reduces streak artifacts in sparse-view CT images.

Offers faster processing with low computational complexity.

Abstract

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.