Abstract
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.
Abstract (translated)
限制角度计算机断层扫描（LACT）常常由于缺少角度信息而面临重大挑战。与以往在图像域操作的方法不同，我们提出了一种新的方法，专注于正弦图的修补（sinogram inpainting）。我们利用MR-SDEs（一种用均值回复随机微分方程描述扩散过程的扩散模型变体），在投影层面填充缺失的角度数据。此外，通过结合蒸馏技术和使用正弦图修补矩阵伪逆来约束模型输出，可以加速扩散过程并在一步内完成，从而实现高效且精确的正弦图补全。随后的一个后处理模块将修补后的正弦图反投射到图像域，并进一步优化重建结果，有效抑制了伪影并保留了关键结构细节。定量实验结果显示，该方法在感知和保真度质量方面均达到了最先进的性能，为LACT重建在科研和临床应用中提供了一种有前景的解决方案。
URL
https://arxiv.org/abs/2505.19385
