Sparse-view CT reduces radiation dose and scanning time by acquiring fewer projection views, but angular undersampling makes reconstruction severely ill-posed, causing streak artifacts, structural blurring, and loss of fine details. Existing supervised methods are often tied to specific sampling settings, whereas generative methods may introduce anatomically inconsistent hallucination-like structures under severe undersampling. We propose Lucid, a sparsity-adaptive, consistency-guided reconstruction framework based on a Flow Matching generative prior for sparse-view CT. Lucid is trained only on high-quality CT images to learn a continuous transport between a Gaussian distribution and the high-quality CT image distribution, independent of view sampling. During inference, the sampling sparsity level is explicitly incorporated to adapt the generative trajectory of a single pretrained model. Specifically, Lucid constructs a degradation-matched initial state by sparsity-weighted fusion of the sparse-view FBP image and Gaussian noise, performs sparsity-modulated Flow Matching updates, and applies projection-domain data-consistency correction after each prior update. Experiments under multiple sparse-view settings show that Lucid achieves stable reconstruction performance across different sampling densities, improves image quality and structural fidelity, and reduces the risk of hallucination-like structures in generative sparse-view CT reconstruction.

翻译：稀疏视角CT通过采集较少投影视角来降低辐射剂量和扫描时间，但角度欠采样使重建问题严重病态，导致条纹伪影、结构模糊和细节丢失。现有监督方法通常受限于特定采样设置，而生成方法在严重欠采样下可能引入解剖结构不一致的幻觉样结构。我们提出Lucid——一种基于流匹配生成先验的稀疏自适应一致性引导重建框架，专用于稀疏视角CT。Lucid仅需高质量CT图像训练，学习高斯分布与高质量CT图像分布之间的连续传输，独立于视角采样。推理时，显式引入采样稀疏度水平以调整单一预训练模型的生成轨迹。具体而言，Lucid通过稀疏度加权融合稀疏视角FBP图像与高斯噪声构建退化匹配初始状态，执行稀疏度调制的流匹配更新，并在每次先验更新后施加投影域数据一致性校正。多组稀疏视角实验表明：Lucid在不同采样密度下均能实现稳定重建性能，提升图像质量与结构保真度，并降低生成式稀疏视角CT重建中幻觉样结构出现的风险。