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

Renxiong Wu1, Shaoyan Huang1, Junming Zhong1

  • 1School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China.

Biomedical Optics Express
|June 21, 2023
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Summary
This summary is machine-generated.

This study introduces MAS-Net OCT, a deep learning approach for optical coherence tomography. It enhances transverse resolution and reduces speckle noise across large depths in biological tissues.

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Area of Science:

  • Biomedical Optics
  • Medical Imaging Technology
  • Machine Learning in Healthcare

Background:

  • Spectral domain optical coherence tomography (SD-OCT) faces a trade-off between transverse resolution and depth of focus (DOF).
  • Speckle noise in OCT degrades image quality and limits resolution enhancement.
  • Multiple aperture synthetic (MAS) OCT extends DOF by synthesizing a larger aperture.

Purpose of the Study:

  • To develop a deep learning-based multiple aperture synthetic OCT (MAS-Net OCT) system.
  • To integrate a self-supervised learning speckle-free model into MAS OCT.
  • To evaluate the performance of MAS-Net OCT in improving resolution and reducing noise.

Main Methods:

  • A novel deep learning framework, MAS-Net, was developed and trained on data from a MAS OCT system.
  • The system integrates a self-supervised learning model for speckle reduction.
  • Experiments were conducted on microparticle samples and various biological tissues.

Main Results:

  • MAS-Net OCT demonstrated significant improvement in transverse resolution over a large imaging depth.
  • The proposed method effectively reduced speckle noise in OCT images.
  • Enhanced imaging quality was observed in both microparticle samples and biological tissues.

Conclusions:

  • MAS-Net OCT successfully overcomes the resolution-DOF compromise inherent in SD-OCT.
  • The integrated speckle-free model significantly improves image clarity.
  • This deep learning approach offers a promising solution for advanced OCT imaging in clinical applications.