利用扩散模型插件式先验进行角膜B扫OCT影像的超分辨率和去噪
Super-Resolution and Denoising of Corneal B-Scan OCT Imaging Using Diffusion Model Plug-and-Play Priors
AI总结：
本文提出利用扩散模型插件式先验提升角膜B扫OCT影像的分辨率和去噪能力，通过贝叶斯逆问题和预训练生成先验实现高保真成像。
摘要：
光学相干断层扫描（OCT）在角膜成像中对于手术计划和诊断至关重要。然而，高速采集往往会降低空间分辨率并增加散斑噪声，给准确解释带来挑战。我们提出了一种先进的超分辨率框架，利用扩散模型插件式（PnP）先验来实现4倍的空间分辨率提升并有效去噪OCT B扫图像。我们的方法将重建建模为一个原则性的贝叶斯逆问题，结合马尔可夫链蒙特卡罗采样与预训练的生成先验，以强制解剖一致性。我们全面验证了该框架使用活体鱼眼角膜数据集，以评估其在不同临床环境下的鲁棒性和可扩展性。与双三次插值、传统监督U-Net基线和替代扩散先验的比较实验表明，我们的方法在解剖结构的精确性、角膜层的清晰度和噪声抑制方面表现更优。定量结果表明在峰值信噪比、结构相似性指数和感知度量方面具有最先进的性能。本工作强调了扩散驱动的插件式重建在提供高保真、高分辨率OCT成像方面的潜力，支持更可靠的临床评估并促进高级图像引导干预。我们的发现表明该方法可以扩展到其他需要稳健超分辨率和去噪的生物医学成像模态。
英文摘要：
Optical coherence tomography (OCT) is pivotal in corneal imaging for both surgical planning and diagnosis. However, high-speed acquisitions often degrade spatial resolution and increase speckle noise, posing challenges for accurate interpretation. We propose an advanced super-resolution framework leveraging diffusion model plug-and-play (PnP) priors to achieve 4x spatial resolution enhancement alongside effective denoising of OCT Bscan images. Our approach formulates reconstruction as a principled Bayesian inverse problem, combining Markov chain Monte Carlo sampling with pretrained generative priors to enforce anatomical consistency. We comprehensively validate the framework using \emph{in vivo} fisheye corneal datasets, to assess robustness and scalability under diverse clinical settings. Comparative experiments against bicubic interpolation, conventional supervised U-Net baselines, and alternative diffusion priors demonstrate that our method consistently yields more precise anatomical structures, improved delineation of corneal layers, and superior noise suppression. Quantitative results show state-of-the-art performance in peak signal-to-noise ratio, structural similarity index, and perceptual metrics. This work highlights the potential of diffusion-driven plug-and-play reconstruction to deliver high-fidelity, high-resolution OCT imaging, supporting more reliable clinical assessments and enabling advanced image-guided interventions. Our findings suggest the approach can be extended to other biomedical imaging modalities requiring robust super-resolution and denoising.
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