通过扩散基于的sinogram补全推进有限角度CT重建
Advancing Limited-Angle CT Reconstruction Through Diffusion-Based Sinogram Completion
AI总结：
本文提出基于扩散模型的sinogram补全方法，结合蒸馏和伪逆约束，实现高效准确的有限角度CT重建，有效抑制伪影并保持结构细节。
摘要：
有限角度计算机断层扫描（LACT）常常由于缺失的角度信息而面临重大挑战。与之前在图像域中操作的方法不同，我们提出了一种新的方法，专注于sinogram补全。我们利用MR-SDEs，一种变异的扩散模型，该模型用均值回归随机微分方程来表征扩散过程，以填补投影层面的缺失角度数据。此外，通过结合蒸馏并利用补全矩阵的伪逆来约束模型的输出，扩散过程被加速并在一步中完成，从而实现高效且准确的sinogram补全。随后的后处理模块将补全的sinogram回投影到图像域并进一步细化重建，有效抑制伪影的同时保持关键结构细节。定量实验结果表明，所提出的方法在感知和保真度质量上均达到最先进的性能，为科学和临床应用中的LACT重建提供了有前景的解决方案。
英文摘要：
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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