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Deep learning-based image enhancement in optical coherence tomography by exploiting interference fringe.

Woojin Lee1, Hyeong Soo Nam1, Jae Yeon Seok2

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This study introduces a deep learning framework to enhance optical coherence tomography (OCT) images by using raw interference fringes. The method improves spatial resolution and reduces noise, offering superior performance over existing techniques.

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Area of Science:

  • Biomedical Imaging
  • Optical Coherence Tomography (OCT)
  • Deep Learning

Background:

  • Optical coherence tomography (OCT) is a non-invasive imaging technique for in vivo biological imaging.
  • OCT's inherent principles limit spatial resolution and signal-to-noise ratio.
  • Existing OCT imaging methods require optimization to overcome performance limitations.

Purpose of the Study:

  • To propose a deep learning-based framework to enhance OCT images.
  • To improve spatial resolution and reduce speckle noise in OCT images by exploiting raw interference fringes.
  • To achieve further image enhancement beyond currently optimized OCT images.

Main Methods:

  • Developed a two-network framework: NetA for A-scans and NetB for B-scans.
  • NetA uses spectrograms from short-time Fourier transform of raw interference fringes to enhance axial resolution.
  • NetB enhances lateral resolution and reduces speckle noise in B-scan images; networks are applied sequentially.

Main Results:

  • The proposed deep learning framework demonstrated robust performance in enhancing OCT images.
  • Visual and quantitative validation confirmed the framework's capability to improve spatial resolution and reduce noise.
  • Comparative studies showed the deep learning approach utilizing interference fringes outperformed existing methods.

Conclusions:

  • The proposed deep learning framework effectively enhances OCT image quality by leveraging raw interference fringes.
  • This method offers significant improvements in spatial resolution and speckle noise reduction compared to conventional techniques.
  • The framework is a versatile technology with the potential to significantly improve OCT functionality.