MAS-Net OCT: a deep-learning-based speckle-free multiple aperture synthetic optical coherence tomography

• Main Authors: Wu, Renxiong, Huang, Shaoyan, Zhong, Junming, Li, Meixuan, Zheng, Fei, Bo, En, Liu, Linbo, Liu, Yong, Ge, Xin, Ni, Guangming
• 其他作者: School of Electrical and Electronic Engineering
• 格式: Article
• 語言: English
• 出版: 2023
• 主題: Engineering::Electrical and electronic engineering; Extended Depth; Focus
• 在線閱讀: https://hdl.handle.net/10356/171482
• 機構: Nanyang Technological University

High-resolution spectral domain optical coherence tomography (SD-OCT) is a vital clinical technique that suffers from the inherent compromise between transverse resolution and depth of focus (DOF). Meanwhile, speckle noise worsens OCT imaging resolving power and restricts potential resolution-enhancement techniques. Multiple aperture synthetic (MAS) OCT transmits light signals and records sample echoes along a synthetic aperture to extend DOF, acquired by time-encoding or optical path length encoding. In this work, a deep-learning-based multiple aperture synthetic OCT termed MAS-Net OCT, which integrated a speckle-free model based on self-supervised learning, was proposed. MAS-Net was trained on datasets generated by the MAS OCT system. Here we performed experiments on homemade microparticle samples and various biological tissues. Results demonstrated that the proposed MAS-Net OCT could effectively improve the transverse resolution in a large imaging depth as well as reduced most speckle noise.