Score-based generative models (SGMs) have gained prominence in sparse-view CT reconstruction for their precise sampling of complex distributions. In SGM-based reconstruction, data consistency in the score-based diffusion model ensures close adherence of generated samples to observed data distribution, crucial for improving image quality. Shortcomings in data consistency characterization manifest in three aspects. Firstly, data from the optimization process can lead to artifacts in reconstructed images. Secondly, it often neglects that the generation model and original data constraints are independently completed, fragmenting unity. Thirdly, it predominantly focuses on constraining intermediate results in the inverse sampling process, rather than ideal real images. Thus, we propose an iterative optimization data scoring model. This paper introduces the data-iterative optimization score-based model (DOSM), integrating innovative data consistency into the Stochastic Differential Equation, a valuable constraint for ultra-sparse-view CT reconstruction. The novelty of this data consistency element lies in its sole reliance on original measurement data to confine generation outcomes, effectively balancing measurement data and generative model constraints. Additionally, we pioneer an inference strategy that traces back from current iteration results to ideal truth, enhancing reconstruction stability. We leverage conventional iteration techniques to optimize DOSM updates. Quantitative and qualitative results from 23 views of numerical and clinical cardiac datasets demonstrate DOSM's superiority over other methods. Remarkably, even with 10 views, our method achieves excellent performance.

翻译：基于得分的生成模型（SGM）因其对复杂分布的精确采样能力，在稀疏视角CT重建中日益受到关注。在基于SGM的重建中，得分扩散模型中的数据一致性确保了生成样本紧密遵循观测数据分布，这对提升图像质量至关重要。数据一致性表征的不足体现在三个方面：首先，优化过程中的数据可能导致重建图像出现伪影；其次，常忽视生成模型与原始数据约束的独立性，破坏整体性；最后，主要约束逆采样过程中的中间结果，而非理想的真实图像。为此，我们提出一种迭代优化数据评分模型。本文引入数据迭代优化得分模型（DOSM），将创新性的数据一致性融入随机微分方程，为超稀疏视角CT重建提供有效约束。该数据一致性要素的创新性在于：仅依赖原始测量数据约束生成结果，有效平衡测量数据与生成模型约束。此外，我们首创一种从当前迭代结果回溯到理想真值的推理策略，增强重建稳定性，并利用传统迭代技术优化DOSM更新。在23视角的数值与临床心脏数据集上的定量与定性结果表明，DOSM优于其他方法。值得注意的是，即使仅用10个视角，我们的方法仍能取得优异性能。