用生成模型提升眼玻璃体OCT图像质量，实现四倍提速。
Deep Generative Models for Enhanced Vitreous OCT Imaging
- 采用条件扩散模型生成高质量玻璃体OCT图像。
- cDDPM在临床评估中表现最优，伪影率仅32.9%。
- 适合眼科影像加速与高质量重建场景。
目的：评估深度学习模型在提升玻璃体光学相干断层扫描（OCT）图像质量及缩短采集时间方面的效果。方法：使用条件去噪扩散概率模型（cDDPM）、布朗桥扩散模型（BBDM）、U-Net、Pix2Pix和向量量化生成对抗网络（VQ-GAN）生成高分辨率频域（SD）玻璃体OCT图像。输入为SD ART10图像，输出与通过平均十个ART10图像得到的伪ART100图像对比。采用图像质量指标和视觉图灵测试评估模型性能，由眼科医生对生成图像进行排名并评估解剖保真度。最佳模型在新采集数据上的手动分割玻璃体区域进一步验证。结果：U-Net在峰值信噪比（PSNR: 30.230）和结构相似性指数（SSIM: 0.820）上最优，其次为cDDPM；Pix2Pix和cDDPM在感知图像相似性（LPIPS）上表现最好。首次视觉图灵测试中cDDPM排名第一（3.07分）；第二次测试中（仅最佳模型），其伪像率32.9%，解剖保留率85.7%。新数据上，cDDPM生成的玻璃体区域在PSNR上更接近ART100参考值，且以ART1为条件时整体图像PSNR高于真实ART1或ART10 B-scan。结论：定量指标与临床评估存在差异，需综合评估。cDDPM在减少采集时间四倍的同时展现生成临床可用图像的潜力。可转化意义：cDDPM具备临床集成前景，支持更快更高质的玻璃体成像。数据与代码将公开。
原文摘要 · Abstract (English)
Purpose: To evaluate deep learning (DL) models for enhancing vitreous optical coherence tomography (OCT) image quality and reducing acquisition time. Methods: Conditional Denoising Diffusion Probabilistic Models (cDDPMs), Brownian Bridge Diffusion Models (BBDMs), U-Net, Pix2Pix, and Vector-Quantised Generative Adversarial Network (VQ-GAN) were used to generate high-quality spectral-domain (SD) vitreous OCT images. Inputs were SD ART10 images, and outputs were compared to pseudoART100 images obtained by averaging ten ART10 images per eye location. Model performance was assessed using image quality metrics and Visual Turing Tests, where ophthalmologists ranked generated images and evaluated anatomical fidelity. The best model's performance was further tested within the manually segmented vitreous on newly acquired data. Results: U-Net achieved the highest Peak Signal-to-Noise Ratio (PSNR: 30.230) and Structural Similarity Index Measure (SSIM: 0.820), followed by cDDPM. For Learned Perceptual Image Patch Similarity (LPIPS), Pix2Pix (0.697) and cDDPM (0.753) performed best. In the first Visual Turing Test, cDDPM ranked highest (3.07); in the second (best model only), cDDPM achieved a 32.9% fool rate and 85.7% anatomical preservation. On newly acquired data, cDDPM generated vitreous regions more similar in PSNR to the ART100 reference than true ART1 or ART10 B-scans and achieved higher PSNR on whole images when conditioned on ART1 than ART10. Conclusions: Results reveal discrepancies between quantitative metrics and clinical evaluation, highlighting the need for combined assessment. cDDPM showed strong potential for generating clinically meaningful vitreous OCT images while reducing acquisition time fourfold. Translational Relevance: cDDPMs show promise for clinical integration, supporting faster, higher-quality vitreous imaging. Dataset and code will be made publicly available.
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