用物理约束的扩散模型修复稀疏视角CT的不完整投影数据
FCDM: A Physics-Guided Bidirectional Frequency Aware Convolution and Diffusion-Based Model for Sinogram Inpainting
- 结合双向频域分析与角度感知掩码，捕捉投影方向特性
- 在真实数据上实现0.93以上SSIM和31 dB以上PSNR
- 适合需要低剂量、快速扫描的纳米CT等医学成像场景
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，但获取全视角投影图需高辐射剂量和长时间扫描。稀疏视角CT虽减轻负担，却导致带有结构性信号丢失的不完整投影图，降低重建质量。与RGB图像不同，投影图编码全局耦合的投影信息并具有方向性频谱特征，传统面向RGB的修补方法（包括扩散模型）因忽略角度依赖性和断层成像的物理约束而失效。本文提出FCDM，一种基于扩散框架的投影图修复方法，融合双向频域推理、角度感知掩码与物理引导正则化，以保持全局结构与物理合理性。在真实世界数据集上的实验表明，FCDM在多种稀疏视角设置下持续优于现有基线，实现超过0.93的SSIM和31 dB的PSNR。
原文摘要 · Abstract (English)
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.
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