Sparse-view Computed Tomography (CT) image reconstruction is a promising approach to reduce radiation exposure, but it inevitably leads to image degradation. Although diffusion model-based approaches are computationally expensive and suffer from the training-sampling discrepancy, they provide a potential solution to the problem. This study introduces a novel Cascaded Diffusion with Discrepancy Mitigation (CDDM) framework, including the low-quality image generation in latent space and the high-quality image generation in pixel space which contains data consistency and discrepancy mitigation in a one-step reconstruction process. The cascaded framework minimizes computational costs by moving some inference steps from pixel space to latent space. The discrepancy mitigation technique addresses the training-sampling gap induced by data consistency, ensuring the data distribution is close to the original manifold. A specialized Alternating Direction Method of Multipliers (ADMM) is employed to process image gradients in separate directions, offering a more targeted approach to regularization. Experimental results across two datasets demonstrate CDDM's superior performance in high-quality image generation with clearer boundaries compared to existing methods, highlighting the framework's computational efficiency.

翻译：稀疏视图计算机断层扫描（CT）图像重建是减少辐射暴露的一种有前景的方法，但不可避免地会导致图像质量下降。尽管基于扩散模型的方法计算成本高且存在训练-采样差异，但它们为此问题提供了潜在解决方案。本研究提出了一种新颖的具有差异缓解机制的级联扩散（CDDM）框架，包括潜在空间中的低质量图像生成和像素空间中的高质量图像生成，其中在一步重建过程中融合了数据一致性与差异缓解。该级联框架通过将部分推理步骤从像素空间迁移到潜在空间，降低了计算成本。差异缓解技术解决了由数据一致性引起的训练-采样差距，确保数据分布接近原始流形。采用专用交替方向乘子法（ADMM）对图像梯度进行分方向处理，实现了更具针对性的正则化方式。在两个数据集上的实验结果表明，与现有方法相比，CDDM在高质量图像生成中具有更清晰的边界，凸显了该框架的计算效率优势。