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.

稀疏视图计算机断层扫描（CT）图像重建是一种重要方法，可以降低辐射暴露，但必然导致图像质量下降。本研究引入了一种新的级联扩散与差异缓解（CDDM）框架，包括潜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，在一个步骤的重建过程中实现了数据一致性和差异缓解。级联框架通过将一些推理步骤从像素空间移动到潜空间，从而降低了计算成本。差异缓解技术解决了由数据一致性引起的训练 - 采样差距问题，确保数据分布接近原始流形。本研究还采用了专用的交替方向乘法器方法（ADMM）来处理图像梯度，提供了一种更有针对性的正则化方法。两个数据集上的实验结果表明，与现有方法相比，CDDM 在高质量图像生成方面具有更清晰的边界，并突出了该框架的计算效率。