arXiv 2509.103412025-09-12cs.CV

GARD：用于视网膜OCT的基于伽马的解剖结构恢复与去噪

GARD: Gamma-based Anatomical Restoration and Denoising for Retinal OCT

Christian Doppler Laboratory for Artificial Intelligence in Retina, Center for Medical Data Science, Medical University of Vienna, 1090 Vienna, Austria（人工视网膜人工智能实验室，医学数据科学中心，维也纳医科大学）；Institute of Artificial Intelligence, Center for Medical Data Science, Medical University of Vienna, 1090 Vienna, Austria（人工智能研究所，医学数据科学中心，维也纳医科大学）

Botond Fazekas, Thomas Pinetz, Guilherme Aresta, Taha Emre, Hrvoje Bogunovic

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AI总结：

本文提出GARD，一种基于去噪扩散伽马模型的视网膜OCT图像去噪方法，通过引入降噪保真项防止高频噪声并加速推理，在多项指标上显著优于现有方法且更好保留解剖细节。

摘要：

光学相干断层扫描（OCT）是诊断和监测视网膜疾病的重要成像方式。然而，OCT图像本质上会受散斑噪声退化影响，这会掩盖细微细节并阻碍准确解读。尽管存在许多去噪方法，但许多方法难以在降低噪声与保留关键解剖结构之间取得平衡。本文提出了GARD（基于伽马的解剖结构恢复与去噪），这是一种用于OCT图像去散斑的新型深度学习方法，它利用了扩散概率模型的优势。与假设高斯噪声的传统扩散模型不同，GARD采用去噪扩散伽马模型，以更准确地反映散斑的统计特性。此外，我们引入了降噪保真项，利用预处理后的低噪声图像来指导去噪过程。这一关键补充防止了高频噪声的重新引入。我们通过将去噪扩散隐式模型框架适配到我们基于伽马的模型中来加速推理过程。在包含成对含噪和低噪声OCT B-scan的数据集上的实验表明，GARD在PSNR、SSIM和MSE方面显著优于传统去噪方法和最先进的深度学习模型。定性结果证实，GARD能产生更清晰的边缘并更好地保留精细解剖细节。

英文摘要：

Optical Coherence Tomography (OCT) is a vital imaging modality for diagnosing and monitoring retinal diseases. However, OCT images are inherently degraded by speckle noise, which obscures fine details and hinders accurate interpretation. While numerous denoising methods exist, many struggle to balance noise reduction with the preservation of crucial anatomical structures. This paper introduces GARD (Gamma-based Anatomical Restoration and Denoising), a novel deep learning approach for OCT image despeckling that leverages the strengths of diffusion probabilistic models. Unlike conventional diffusion models that assume Gaussian noise, GARD employs a Denoising Diffusion Gamma Model to more accurately reflect the statistical properties of speckle. Furthermore, we introduce a Noise-Reduced Fidelity Term that utilizes a pre-processed, less-noisy image to guide the denoising process. This crucial addition prevents the reintroduction of high-frequency noise. We accelerate the inference process by adapting the Denoising Diffusion Implicit Model framework to our Gamma-based model. Experiments on a dataset with paired noisy and less-noisy OCT B-scans demonstrate that GARD significantly outperforms traditional denoising methods and state-of-the-art deep learning models in terms of PSNR, SSIM, and MSE. Qualitative results confirm that GARD produces sharper edges and better preserves fine anatomical details.

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