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

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.Published versionThis work was funded by National Natural Science Foundation of China (61905036); China Postdoctoral Science Foundation (2019M663465, 2021T140090); Medico-Engineering Cooperation Funds from University of Electronic Science and Technology of China (ZYGX2021YGCX019); Fundamental Research Funds for the Central Universities (ZYGX2021J012)

S32 3D images of Fig.8(i)

Visualization S32 3D images of Fig.8(i