Anterior segment optical coherence tomography (AS-OCT) is a non-invasive imaging technique that is highly valuable for ophthalmic diagnosis. However, speckles in AS-OCT images can often degrade the image quality and affect clinical analysis. As a result, removing speckles in AS-OCT images can greatly benefit automatic ophthalmology analysis. Unfortunately, challenges still exist in deploying effective AS-OCT image denoising algorithms, including collecting sufficient paired training data and the requirement to preserve consistent content in medical images. To address these practical issues, we propose an unsupervised AS-OCT despeckling algorithm via Content Preserving Diffusion Model (CPDM) with statistical knowledge. At the training stage, a Markov chain transforms clean images to white Gaussian noise by repeatedly adding random noise and removes the predicted noise in a reverse procedure. At the inference stage, we first analyze the statistical distribution of speckles and convert it into a Gaussian distribution, aiming to match the fast truncated reverse diffusion process. We then explore the posterior distribution of observed images as a fidelity term to ensure content consistency in the iterative procedure. Our experimental results show that CPDM significantly improves image quality compared to competitive methods. Furthermore, we validate the benefits of CPDM for subsequent clinical analysis, including ciliary muscle (CM) segmentation and scleral spur (SS) localization.

翻译：眼前节光学相干断层扫描（AS-OCT）是一种非侵入性成像技术，在眼科诊断中具有重要价值。然而，AS-OCT图像中的散斑常会降低图像质量并影响临床分析。因此，去除AS-OCT图像中的散斑可显著促进自动化眼科分析。目前，部署有效的AS-OCT图像去噪算法仍面临挑战，包括收集充足的配对训练数据以及保持医学图像内容一致性的要求。为解决这些实际问题，我们提出了一种基于统计知识的内容保持扩散模型（CPDM）的无监督AS-OCT去斑算法。在训练阶段，通过马尔可夫链反复添加随机噪声将清晰图像转换为白高斯噪声，并在反向过程中逐步去除预测噪声。在推理阶段，我们首先分析散斑的统计分布并将其转化为高斯分布，以匹配快速截断的逆向扩散过程。接着，我们将观测图像的后验分布作为保真项，确保迭代过程中的内容一致性。实验结果表明，与竞争方法相比，CPDM显著提升了图像质量。此外，我们验证了CPDM在后续临床分析中的优势，包括睫状肌（CM）分割和巩膜突（SS）定位。