用扩散模型补全缺失角度的投影数据，提升有限角CT成像质量。
Advancing Limited-Angle CT Reconstruction Through Diffusion-Based Sinogram Completion
- 在投影域用扩散模型填补缺失角度数据
- 一次推断完成补全，速度更快且精度更高
- 适合医学影像和科学计算中的有限角CT重建
有限角计算机断层扫描（LACT）因缺少角度信息而面临严重挑战。与以往在图像域操作的方法不同，本文提出一种在投影域进行正弦图补全的新方法。利用具有均值回复随机微分方程的MR-SDE扩散模型，在投影层面填充缺失角度数据。通过结合知识蒸馏与使用补全矩阵伪逆约束输出，使扩散过程加速并实现一步完成，从而高效准确地完成正弦图补全。随后的后处理模块将补全后的正弦图反投影至图像域，并进一步优化重建结果，有效抑制伪影同时保留关键结构细节。定量实验表明，该方法在感知质量和保真度上均达到当前最优水平，为科学与临床应用中的LACT重建提供了有前景的解决方案。
原文摘要 · Abstract (English)
Limited Angle Computed Tomography (LACT) often faces significant challenges due to missing angular information. Unlike previous methods that operate in the image domain, we propose a new method that focuses on sinogram inpainting. We leverage MR-SDEs, a variant of diffusion models that characterize the diffusion process with mean-reverting stochastic differential equations, to fill in missing angular data at the projection level. Furthermore, by combining distillation with constraining the output of the model using the pseudo-inverse of the inpainting matrix, the diffusion process is accelerated and done in a step, enabling efficient and accurate sinogram completion. A subsequent post-processing module back-projects the inpainted sinogram into the image domain and further refines the reconstruction, effectively suppressing artifacts while preserving critical structural details. Quantitative experimental results demonstrate that the proposed method achieves state-of-the-art performance in both perceptual and fidelity quality, offering a promising solution for LACT reconstruction in scientific and clinical applications.
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