VET: Vasculature Extraction Transformer for Single-Scan Optical Coherence Tomography Angiography

Abstract

Optical coherence tomography angiography (OCTA) is a non-invasive imaging modality for analyzing skin microvasculature, enabling non-invasive diagnosis and treatment monitoring. Traditional OCTA algorithms necessitate at least two-repeated scans to generate microvasculature images, while image quality is highly dependent on the repetitions of scans (e.g., 4-8). Nevertheless, a higher repetition count increases data acquisition time, causing patient discomfort and more unpredictable motion artifacts, which can result in potential misdiagnosis. To address these limitations, we proposed a vasculature extraction pipeline based on the novelty vasculature extraction transformer (VET) to generate OCTA images by using a single OCT scan. Distinct from the vision Transformer, VET utilizes convolutional projection to better learn the spatial relationships between image patches. This study recruited 15 healthy participants. The OCT scans were performed in five various skin sites, i.e., palm, arm, face, neck, and lip. Our results show that in comparison to OCTA images obtained by the speckle variance OCTA (peak-signal-to-noise ratio (PSNR): 16.13) and eigen-decomposition OCTA (PSNR: 17.08) using four repeated OCT scans, OCTA images extracted by the proposed pipeline exhibit a better PSNR (18.03) performance while reducing the data acquisition time by 75%. Visual comparisons show that the proposed pipeline outperformed traditional OCTA algorithms, particularly in the imaging of lip and face areas, where artifacts are commonly encountered. This study is the first to demonstrate that the VET can efficiently extract high-quality vasculature images from a single, rapid OCT scan. This capability significantly enhances diagnostic accuracy for patients and streamlines the imaging process.