Liao Jinpeng, Yang Shufan, Zhang Tianyu, Li Chunhui, Huang Zhihong
School of Science and Engineering, University of Dundee, DD1 4HN, Scotland, UK.
Engineering and Built Environment, Edinburgh Napier University, Edinburgh, UK.
Biomed Opt Express. 2023 Jul 6;14(8):3899-3913. doi: 10.1364/BOE.486933. eCollection 2023 Aug 1.
Traditional high-quality OCTA images require multi-repeated scans (e.g., 4-8 repeats) in the same position, which may cause the patient to be uncomfortable. We propose a deep-learning-based pipeline that can extract high-quality OCTA images from only two-repeat OCT scans. The performance of the proposed image reconstruction U-Net (IRU-Net) outperforms the state-of-the-art UNet vision transformer and UNet in OCTA image reconstruction from a two-repeat OCT signal. The results demonstrated a mean peak-signal-to-noise ratio increased from 15.7 to 24.2; the mean structural similarity index measure improved from 0.28 to 0.59, while the OCT data acquisition time was reduced from 21 seconds to 3.5 seconds (reduced by 83%).
传统的高质量光学相干断层扫描血管造影（OCTA）图像需要在同一位置进行多次重复扫描（例如4 - 8次重复），这可能会导致患者感到不适。我们提出了一种基于深度学习的流程，该流程仅通过两次重复的OCT扫描就能提取高质量的OCTA图像。所提出的图像重建U型网络（IRU-Net）在从两次重复的OCT信号进行OCTA图像重建方面的性能优于当前最先进的U型网络视觉变换器和U型网络。结果表明，平均峰值信噪比从15.7提高到24.2；平均结构相似性指数测量从0.28提高到0.59，同时OCT数据采集时间从21秒减少到3.5秒（减少了83%）。
