Diffusion model based OCT to OCTA translation

This study introduces a conditional diffusion-based approach (Brown Bridge diffusion model, BBDM) for translating optical coherence tomography (OCT) images into OCT Angiography (OCTA).

Traditional generative adversarial networks (GANs) often face limitations in generalization and structural fidelity due to adversarial loss and one-to-one mappings. In contrast, BBDM employs a bidirectional stochastic process that transitions directly between OCT and OCTA without intermediate conditioning, improving robustness, generalizability and structural consistency. The model was implemented in the latent space of VQGAN, trained on the OCT500 dataset and evaluated on an independent clinical dataset from the University of Illinois at Chicago (UIC) comprising diabetic retinopathy patients with varying severity.

Quantitative vascular features-blood vessel density (BVD), caliber (BVC), tortuosity (BVT) and vessel perimeter index (VPI) along with image-quality metrics such as structural similarity index (SSIM), Fréchet inception distance (FID), and perceptual contrast quality index (PCQI) were used for evaluation. BBDM achieved higher SSIM and PCQI scores in larger field-of-view scans, indicating improved structural preservation and perceptual fidelity compared to GAN. Although it slightly underperformed in FID and showed variability in vascular features, BBDM maintained anatomical trends consistent with ground-truth OCTA. Moreover, it reliably preserved clinically relevant features such as BVC, BVT, and VPI. Despite minor feature-level deviations, BBDM offers advantages in computational simplicity, training stability and reduced hallucinations.

This work presents the first diffusion-based framework for OCT-to-OCTA translation and demonstrates that BBDM can generate clinically meaningful OCTA from standard OCT, supporting more accessible and cost-effective retinal disease diagnostics.