用可学习优化算法提升低剂量CT重建质量，减少伪影、保留细节。
Learnable Optimization-Based Algorithms for Low-Dose CT Reconstruction
- 将深度学习嵌入变分模型，通过图像与投影数据联合优化增强正则化
- 在真实临床数据上显著降低伪影，细节保留优于传统方法
- 适合医学影像重建、放射科医生及算法研发者参考
低剂量计算机断层扫描（LDCT）旨在降低患者辐射暴露的同时保持诊断级图像质量。然而，传统重建算法常因问题病态性导致严重图像伪影。近年来，基于优化的深度学习方法为改善LDCT重建提供了新思路。本文探索了可学习优化算法（LOA），将深度学习融入变分模型以增强正则化过程。所提出的LEARN++和MAGIC等方法采用双域网络，联合优化图像与投影数据，显著提升重建质量；同时设计了基于近端梯度下降和ADMM启发的网络，兼具高效性与理论依据。实验表明，这些可学习方法在临床场景中表现优异，相比传统技术实现更优的伪影抑制、更佳的细节保留能力。
原文摘要 · Abstract (English)
Low-dose computed tomography (LDCT) aims to minimize the radiation exposure to patients while maintaining diagnostic image quality. However, traditional CT reconstruction algorithms often struggle with the ill-posed nature of the problem, resulting in severe image artifacts. Recent advances in optimization-based deep learning algorithms offer promising solutions to improve LDCT reconstruction. In this paper, we explore learnable optimization algorithms (LOA) for CT reconstruction, which integrate deep learning within variational models to enhance the regularization process. These methods, including LEARN++ and MAGIC, leverage dual-domain networks that optimize both image and sinogram data, significantly improving reconstruction quality. We also present proximal gradient descent and ADMM-inspired networks, which are efficient and theoretically grounded approaches. Our results demonstrate that these learnable methods outperform traditional techniques, offering enhanced artifact reduction, better detail preservation, and robust performance in clinical scenarios.
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