Affiliation:
1. University of Electronic Science and Technology of China
2. Nanyang Technological University
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.
Funder
National Natural Science Foundation of China
China Postdoctoral Science Foundation
Medico-Engineering Cooperation Funds from University of Electronic Science and Technology of China
Fundamental Research Funds for the Central Universities
Subject
Atomic and Molecular Physics, and Optics,Biotechnology
Cited by
3 articles.
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