Title: DICE: Diffusion Consensus Equilibrium for Sparse-view CT Reconstruction Show Chinese title

Title: DICE：稀疏视图CT重建的扩散共识平衡

Abstract: 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. Show Chinese abstract

Abstract: 稀疏视角计算机断层扫描（CT）重建由于欠采样而具有根本性挑战，导致一个病态的逆问题。 传统迭代方法结合手工设计或学习到的先验信息来正则化解决方案，但难以捕捉医学图像中的复杂结构。 相比之下，扩散模型（DMs）最近作为强大的生成先验出现，能够准确建模复杂的图像分布。 在本工作中，我们引入了扩散共识平衡（DICE），这是一种将双代理共识平衡整合到DM采样过程中的框架。 DICE在以下两个步骤之间交替进行：（i）数据一致性代理，通过强制测量一致性的近似算子实现，以及（ii）先验代理，通过DM在每个采样步骤中执行干净图像估计来实现。 通过迭代平衡这两个互补代理，DICE有效地结合了强大的生成先验能力与测量一致性。 实验结果表明，在15、30和60视角（总共180视角）的均匀和非均匀稀疏视角设置下，DICE在重建高质量CT图像方面显著优于最先进的基线方法，证明了其有效性和鲁棒性。