  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
implementing a generative adversarial network framework that includes a 2D
vascular segmentation model and a 2D OCTA image translation model. 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). We then quantitatively
characterize vascular features generated in TR-OCTAs with GT-OCTAs to assess
the feasibility of using TR-OCTA for objective disease diagnosis. 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 a better trend compared to density features which are
affected by local vascular distortions. Conclusion: This study presents a
promising solution to the limitations of OCTA adoption in clinical practice by
using vascular features from TR-OCTA for disease detection. 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.
