Computed tomography (CT) is widely used in scientific imaging systems such as synchrotron and laboratory-based nano-CT, but acquiring full-view sinograms requires high radiation dose and long scan times. Sparse-view CT reduces this burden but produces incomplete sinograms with structured signal loss, degrading reconstruction quality. Unlike RGB images, sinograms encode globally coupled projections and exhibit directional spectral patterns, making conventional RGB-oriented inpainting methods, including diffusion models, ineffective because they ignore angular dependencies and physical constraints inherent to tomographic data. We propose FCDM, a diffusion-based framework for sinogram restoration that incorporates bidirectional frequency reasoning, angular-aware masking, and physics-guided regularization to preserve global structure and physical plausibility. Experiments on real-world datasets show that FCDM consistently outperforms existing baselines, achieving over 0.93 SSIM and 31 dB PSNR across diverse sparse-view settings.

翻译：计算机断层扫描（CT）广泛应用于同步辐射和实验室纳米CT等科学成像系统，但获取完整视角的正弦图需要高辐射剂量和长扫描时间。稀疏视图CT减轻了这一负担，但会产生具有结构化信号缺失的不完整正弦图，从而降低重建质量。与RGB图像不同，正弦图编码了全局耦合的投影并表现出方向性频谱模式，这使得包括扩散模型在内的传统面向RGB的修复方法效果不佳，因为它们忽略了断层扫描数据固有的角度依赖性和物理约束。我们提出了FCDM，一种用于正弦图恢复的基于扩散的框架，它结合了双向频率推理、角度感知掩蔽和物理引导正则化，以保持全局结构和物理合理性。在真实数据集上的实验表明，FCDM在不同稀疏视图设置下始终优于现有基线，实现了超过0.93的SSIM和31 dB的PSNR。