Progressively Texture-Aware Diffusion for Contrast-Enhanced Sparse-View CT
For medical imaging, this work improves sparse-view CT reconstruction by enhancing texture consistency and fidelity, though it is an incremental improvement over existing diffusion-based methods.
The paper tackles the challenge of recovering reliable image content and visually consistent textures in sparse-view CT imaging. The proposed Progressively Texture-aware Diffusion (PTD) model achieves superior performance in structure similarity and visual appeal with only a few sampling steps, mitigating randomness in general diffusion models.
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.