用渐进式扩散模型提升稀疏视角CT的纹理清晰度与图像质量。
Progressively Texture-Aware Diffusion for Contrast-Enhanced Sparse-View CT
- 分步重建：先恢复低频内容，再用条件扩散生成高保真纹理。
- 仅用少量采样步数即实现结构相似性高、视觉效果好。
- 适合需要高质量医学影像重建的研究者和临床应用。
基于扩散模型的稀疏视角计算机断层扫描（SVCT）成像近年来取得显著进展，得益于其更稳定的生成能力。然而，恢复可靠图像内容和视觉一致的纹理仍是关键挑战。本文提出一种渐进式纹理感知扩散模型（PTD），采用从粗到细的学习框架，包含基础重建模块PTD$_{\textit{rec}}$和条件扩散模块PTD$_{\textit{diff}}$。PTD$_{\textit{rec}}$首先学习确定性映射，恢复大部分低频信号（即平滑纹理的粗略内容），作为初始估计以保障保真度；PTD$_{\textit{diff}}$则在粗略预测基础上，通过双域引导的条件扩散生成高保真细节，确保纹理真实且一致。大量实验表明，所提PTD在稀疏视角CT重建中表现优异，结构相似性高、视觉吸引力强，且仅需少量采样步骤，有效缓解了通用扩散模型固有的随机性，实现了视觉质量与高频细节保真度之间的更好权衡。
原文摘要 · Abstract (English)
Diffusion-based sparse-view CT (SVCT) imaging has achieved remarkable advancements in recent years, thanks to its more stable generative capability. However, recovering reliable image content and visually consistent textures is still a crucial challenge. In this paper, we present a Progressively Texture-aware Diffusion (PTD) model, a coarse-to-fine learning framework tailored for SVCT. Specifically, PTD comprises a basic reconstructive module PTD$_{\textit{rec}}$ and a conditional diffusion module PTD$_{\textit{diff}}$. PTD$_{\textit{rec}}$ first learns a deterministic mapping to recover the majority of the underlying low-frequency signals (i.e., coarse content with smoothed textures), which serves as the initial estimation to enable fidelity. Moreover, PTD$_{\textit{diff}}$ aims to reconstruct high-fidelity details for coarse prediction, which explores a dual-domain guided conditional diffusion to generate reliable and consistent textures. Extensive experiments on sparse-view CT reconstruction demonstrate that our PTD achieves superior performance in terms of structure similarity and visual appeal with only a few sampling steps, which mitigates the randomness inherent in general diffusion models and enables a better trade-off between visual quality and fidelity of high-frequency details.
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