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.

翻译：光学相干断层扫描血管成像（OCTA）是一种非侵入性成像模态，通过从周围静态生物组织中提取运动的红细胞信号，扩展了OCT的功能。OCTA已成为分析皮肤微血管系统的有力工具，能够实现更准确的诊断和治疗监测。现有的大多数OCTA提取算法，如散斑方差（SV）和特征分解（ED）OCTA，需要在同一位置进行大量重复（NR）OCT扫描以生成高质量的血管造影图像。然而，较高的NR会延长数据采集时间，从而导致更多不可预测的运动伪影。本研究提出了一种仅使用单次重复OCT扫描即可生成OCTA图像的血管提取流程。该流程基于所提出的血管提取Transformer（VET），利用卷积投影更好地学习图像块之间的空间关系。与使用四次重复OCT扫描获得的SV-OCTA（PSNR：17.809）和ED-OCTA（PSNR：18.049）图像相比，VET提取的OCTA图像表现出中等质量（PSNR：17.515）和更高的图像对比度，同时将所需数据采集时间从约8秒缩短至约2秒。基于视觉观察，所提出的VET在处理难以扫描的颈部和面部OCTA数据时，性能优于SV和ED算法。本研究证明VET有能力从快速单次重复OCT扫描中提取血管图像，从而促进患者的准确诊断。