  Optical coherence tomography angiography (OCTA) is a non-invasive imaging
modality that extends the functionality of OCT by extracting moving red blood
cell signals from surrounding static biological tissues. OCTA has emerged as a
valuable tool for analyzing skin microvasculature, enabling more accurate
diagnosis and treatment monitoring. Most existing OCTA extraction algorithms,
such as speckle variance (SV)- and eigen-decomposition (ED)-OCTA, implement a
larger number of repeated (NR) OCT scans at the same position to produce
high-quality angiography images. However, a higher NR requires a longer data
acquisition time, leading to more unpredictable motion artifacts. In this
study, we propose a vasculature extraction pipeline that uses only one-repeated
OCT scan to generate OCTA images. The pipeline is based on the proposed
Vasculature Extraction Transformer (VET), which leverages convolutional
projection to better learn the spatial relationships between image patches. In
comparison to OCTA images obtained via the SV-OCTA (PSNR: 17.809) and ED-OCTA
(PSNR: 18.049) using four-repeated OCT scans, OCTA images extracted by VET
exhibit moderate quality (PSNR: 17.515) and higher image contrast while
reducing the required data acquisition time from ~8 s to ~2 s. Based on visual
observations, the proposed VET outperforms SV and ED algorithms when using neck
and face OCTA data in areas that are challenging to scan. This study represents
that the VET has the capacity to extract vascularture images from a fast
one-repeated OCT scan, facilitating accurate diagnosis for patients.
