Purpose This study explores the feasibility of using generative machine learning (ML) to translate Optical Coherence Tomography (OCT) images into Optical Coherence Tomography Angiography (OCTA) images, potentially bypassing the need for specialized OCTA hardware.

Methods The method involved a generative adversarial network framework that includes a 2D vascular segmentation model and a 2D OCTA image translation model. This framework is designed to enhance the accuracy, resolution, and continuity of vascular regions in the translated OCTA (TR-OCTA) images. The study utilizes a public dataset of 500 patients, divided into subsets based on resolution and disease status, to validate the quality of TR-OCTA images. The validation employs several quality and quantitative metrics to compare the translated images with ground truth OCTAs (GT-OCTA).

Result TR-OCTAs showed high image quality in both 3 and 6 mm datasets (high-resolution, moderate structural similarity and contrast quality compared to GT-OCTAs). There were slight discrepancies in vascular metrics, especially in diseased patients. Blood vessel features like tortuosity and vessel perimeter index showed better trend compared to density feature which is affected by local vascular distortions.

Conclusion This study presents a promising solution to the limitations of OCTA adoption in clinical practice by using ML to translate OCT data into OCTA images.

Translation relevance This study has the potential to significantly enhance the diagnostic process for retinal diseases by making detailed vascular imaging more widely available and reducing dependency on costly OCTA equipment.

The authors have declared no competing interest.

This study was funded by NEI R15EY035804 (MNA) and UNC Charlotte Faculty Research Grant (MNA)

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The study used (or will use) ONLY openly available human data that were originally located at: https://ieee-dataport.org/open-access/octa-500

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Funding: Supported by NEI R15EY035804 (MNA) and UNC Charlotte Faculty Research Grant (MNA).

Commercial Relationship Disclosure: There is no commercial relationship from any authors related to this research work.

All data produced in the present work are contained in the manuscript