用语义对比学习提升低剂量正交CT重建质量，减少伪影。
Semantic contrastive learning for orthogonal X-ray computed tomography reconstruction
- 分阶段U-Net架构：粗重建、细节优化、语义相似性评估
- 在胸部数据集上显著降低伪影，图像质量优于现有方法
- 适合医疗影像领域研究者，尤其关注低剂量成像的场景
X射线计算机断层扫描（CT）在医学影像中广泛应用，稀疏视角重建可有效降低辐射剂量。然而，病态问题常导致严重条纹伪影。基于深度学习的方法虽提升了重建质量，但仍面临挑战。本文提出一种新型语义特征对比学习损失函数，在高层潜在空间评估语义相似性，在浅层潜在空间评估解剖相似性。采用三阶段U-Net架构：分别用于粗重建、细节细化和语义相似性测量。在包含正交投影的胸部数据集上测试表明，该方法在图像质量与处理速度上均优于其他算法。结果表明，该方法在保持低计算复杂度的同时显著提升重建效果，为正交CT重建提供了实用解决方案。
原文摘要 · Abstract (English)
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.
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