用扩散模型提升OCT低采样率成像质量，细节更清晰、噪声更少。
Super-Resolution Optical Coherence Tomography Using Diffusion Model-Based Plug-and-Play Priors
- 将扩散模型作为先验，结合马尔可夫链蒙特卡洛推断图像重建。
- 在活体和离体鱼眼角膜数据上，分辨率优于传统2D-UNet。
- 适合高速采集场景下的临床高保真OCT成像应用。
我们提出一种基于插件式扩散模型（PnP-DM）的OCT超分辨率框架，用于从稀疏测量中重建高质量图像（OCT B-mode角膜图像）。该方法将重建建模为逆问题，结合扩散先验与马尔可夫链蒙特卡洛采样，实现高效的后验推断。通过采集高速欠采样角膜B模式图像，并利用深度学习上采样流程构建真实训练对。在活体及离体鱼眼角膜模型上的评估显示，PnP-DM优于传统2D-UNet基线，生成更锐利的结构并具有更好的噪声抑制效果。该方法推动了高速采集下高保真OCT成像在临床中的应用。
原文摘要 · Abstract (English)
We propose an OCT super-resolution framework based on a plug-and-play diffusion model (PnP-DM) to reconstruct high-quality images from sparse measurements (OCT B-mode corneal images). Our method formulates reconstruction as an inverse problem, combining a diffusion prior with Markov chain Monte Carlo sampling for efficient posterior inference. We collect high-speed under-sampled B-mode corneal images and apply a deep learning-based up-sampling pipeline to build realistic training pairs. Evaluations on in vivo and ex vivo fish-eye corneal models show that PnP-DM outperforms conventional 2D-UNet baselines, producing sharper structures and better noise suppression. This approach advances high-fidelity OCT imaging in high-speed acquisition for clinical applications.
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