A Fast Optical Coherence Tomography Angiography Image Acquisition and Reconstruction Pipeline for Skin Application

Liao, Jinpeng; Yang, Shufan; Zhang, Tianyu; Li, Chunhui; Huang, Zhihong

Abstract

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%).

Citation

Liao, J., Yang, S., Zhang, T., Li, C., & Huang, Z. (2023). A Fast Optical Coherence Tomography Angiography Image Acquisition and Reconstruction Pipeline for Skin Application. Biomedical Optics Express, 14(8), 3899-3913. https://doi.org/10.1364/BOE.486933

• Journal Article Type: Article
• Acceptance Date: Apr 21, 2023
• Online Publication Date: Jul 6, 2023
• Publication Date: 2023
• Deposit Date: Apr 25, 2023
• Publicly Available Date: Apr 25, 2023
• Publisher: Optical Society of America
• Peer Reviewed: Peer Reviewed
• Volume: 14
• Issue: 8
• Pages: 3899-3913
• DOI: https://doi.org/10.1364/BOE.486933
• Publisher URL: https://opg.optica.org/boe/home.cfm