缓解级联扩散模型中数据一致性引起的差异用于稀疏视图CT重建
Mitigating Data Consistency Induced Discrepancy in Cascaded Diffusion Models for Sparse-view CT Reconstruction
AI总结：
提出CDDM框架，通过级联潜空间与像素空间生成并缓解数据一致性差异，实现稀疏视图CT重建的高质量图像生成与计算效率提升。
摘要：
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，但它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昂且存在训练-采样差异，但它们为该问题提供了潜在解决方案。本研究提出了一种新颖的级联扩散与差异缓解（CDDM）框架，包括在潜空间中的低质量图像生成和在像素空间中的高质量图像生成，后者在一步重建过程中包含数据一致性和差异缓解。级联框架通过将部分推理步骤从像素空间转移到潜空间来最小化计算成本。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。采用专门的交替方向乘子法（ADMM）分别处理不同方向的图像梯度，提供更有针对性的正则化方法。在两个数据集上的实验结果表明，与现有方法相比，CDDM在高质量图像生成方面具有更清晰的边界，突显了该框架的计算效率。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
