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Diffusion model based OCT to OCTA translation
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DOI：10.3389/fmed.2025.1655453.png)
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Abstract
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IntroductionThis study introduces a conditional diffusion-based approach (Brown Bridge diffusion model; BBDM) for translating optical coherence tomography (OCT) images into OCT Angiography (OCTA).MethodsTraditional 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.ResultsQuantitative 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.ConclusionThis 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.
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