ORIGINAL RESEARCH article
Front. Med.
Sec. Ophthalmology
This article is part of the Research TopicInnovative Advancements in Eye Image Processing for Improved Ophthalmic DiagnosisView all 6 articles
Diffusion Model Based OCT to OCTA Translation
Provisionally accepted
- 1University of North Carolina at Charlotte, Charlotte, United States
- 2Department of Surgical Ophthalmology, Atrium Health Wake Forest Baptist, Winston-Salem, United States
- 3Department of Ophthalmology and Visual Science, University of Illinois at Chicago, Chicago, United States
- 4Department of Ophthalmology, Stanford University School of Medicine, Stanford, United States
- 5Department of Electrical and Computer Engineering, UNC Charlotte, Charlotte, United States
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Introduction: This study introduces a conditional diffusion-based approach (Brown Bridge diffusion model, BBDM) for translating optical coherence tomography (OCT) images into OCT Angiography (OCTA). Methods: 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. Results: 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. Conclusion: 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.
Keywords: diffusion model, BBDM, translation, OCT, OCTA, GaN, Vascular features
Received: 27 Jun 2025; Accepted: 18 Nov 2025.
Copyright: © 2025 Hasan Badhon, Thompson, Lim, Leng and Alam. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Rashadul Hasan Badhon, rbadhon@uncc.edu
Minhaj Nur Alam, minhaj.alam@charlotte.edu
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