Quantitative characterization of retinal features in translated OCTA

This study explores the feasibility of quantitative Optical Coherence Tomography Angiography (OCTA) features translated from OCT using generative machine learning (ML) for characterizing vascular changes in retina. A generative adversarial network framework was employed alongside a 2D vascular segmentation and a 2D OCTA image translation model, trained on the OCT-500 public dataset and validated with data from the University of Illinois at Chicago (UIC) retina clinic. Datasets are categorized by scanning range (Field of view) and disease status. Validation involved quality and quantitative metrics, comparing translated OCTA (TR-OCTA) with ground truth OCTAs (GT-OCTA) to assess the feasibility for objective disease diagnosis. In our study, TR-OCTAs showed high image quality in both 3 and 6 mm datasets (high-resolution and contrast quality, moderate structural similarity compared to GT-OCTAs). Vascular features like tortuosity and vessel perimeter index exhibits more consistent trends compared to density features which are affected by local vascular distortions. For the validation dataset (UIC), the metrics show similar trend with a slightly decreased performance since the model training was blind on UIC data, to evaluate inference performance. Overall, 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. By making detailed vascular imaging more widely accessible and reducing reliance on expensive OCTA equipment, this research has the potential to significantly enhance the diagnostic process for retinal diseases.