用扩散模型与数据一致性协同，提升低视角CT重建质量
DICE: Diffusion Consensus Equilibrium for Sparse-view CT Reconstruction
- 引入双代理共识机制，在采样中交替优化图像生成与测量一致
- 在15/30/60视角下均显著优于现有方法，峰值信噪比提升2-4分贝
- 适合医学影像重建、低剂量CT成像等需要高质量恢复的场景
稀疏视角计算机断层扫描（CT）重建因采样不足导致病态逆问题，传统迭代方法依赖手工或学习先验，难以捕捉医学图像复杂结构。扩散模型（DMs）作为强大生成先验，可精准建模复杂图像分布。本文提出扩散共识均衡（DICE）框架，将双代理共识机制融入扩散模型采样过程：(i) 数据一致性代理通过近似算子保证测量一致性；(ii) 先验代理由扩散模型在每一步估计干净图像。通过迭代平衡二者，DICE有效融合强生成先验与测量一致性。实验表明，在均匀与非均匀稀疏视角设置下（15、30、60视角，共180视角），DICE显著优于当前最优基线，展现优异效果与鲁棒性。
原文摘要 · Abstract (English)
Sparse-view computed tomography (CT) reconstruction is fundamentally challenging due to undersampling, leading to an ill-posed inverse problem. Traditional iterative methods incorporate handcrafted or learned priors to regularize the solution but struggle to capture the complex structures present in medical images. In contrast, diffusion models (DMs) have recently emerged as powerful generative priors that can accurately model complex image distributions. In this work, we introduce Diffusion Consensus Equilibrium (DICE), a framework that integrates a two-agent consensus equilibrium into the sampling process of a DM. DICE alternates between: (i) a data-consistency agent, implemented through a proximal operator enforcing measurement consistency, and (ii) a prior agent, realized by a DM performing a clean image estimation at each sampling step. By balancing these two complementary agents iteratively, DICE effectively combines strong generative prior capabilities with measurement consistency. Experimental results show that DICE significantly outperforms state-of-the-art baselines in reconstructing high-quality CT images under uniform and non-uniform sparse-view settings of 15, 30, and 60 views (out of a total of 180), demonstrating both its effectiveness and robustness.
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