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

Renxiong Wu; Shaoyan Huang; Junming Zhong; Meixuan Li; Fei Zheng; En Bo; Linbo Liu; Yong Liu; Xin Ge; Guangming Ni

doi:10.1364/BOE.483740

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

Biomed Opt Express. 2023 May 10;14(6):2591-2607. doi: 10.1364/BOE.483740. eCollection 2023 Jun 1.

Authors

Renxiong Wu¹, Shaoyan Huang¹, Junming Zhong¹, Meixuan Li¹, Fei Zheng¹, En Bo², Linbo Liu², Yong Liu¹, Xin Ge³, Guangming Ni¹

Affiliations

¹ School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China.
² School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore.
³ School of Science, Shenzhen Campus of Sun Yat-sen University, Shenzhen 510275, China.

Abstract

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.