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Neural network-based image reconstruction in swept-source optical coherence tomography using undersampled spectral

Yijie Zhang1,2,3, Tairan Liu1,2,3, Manmohan Singh4

  • 1Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA.

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This study introduces a deep learning framework for faster optical coherence tomography (OCT) imaging. The method reconstructs high-quality swept-source OCT images from undersampled spectral data, reducing acquisition time without artifacts.

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Area of Science:

  • Biomedical Imaging
  • Optical Coherence Tomography
  • Deep Learning

Background:

  • Optical coherence tomography (OCT) is a key non-invasive imaging technique for rapid volumetric data acquisition.
  • Current OCT systems face limitations in imaging speed and data volume.
  • Spectral undersampling in OCT can lead to spatial aliasing artifacts.

Purpose of the Study:

  • To develop and validate a deep learning-based framework for reconstructing swept-source OCT (SS-OCT) images from undersampled spectral data.
  • To demonstrate artifact removal and preservation of image quality with reduced data acquisition.
  • To explore optimization strategies for spectral undersampling in OCT.

Main Methods:

  • A deep neural network was trained and tested for image reconstruction using SS-OCT data from mouse embryos.
  • The framework processed 2-fold and 3-fold undersampled spectral data, reconstructing images without spatial aliasing.
  • An A-line-optimized undersampling method was developed by jointly optimizing spectral sampling and network reconstruction.

Main Results:

  • The deep learning framework successfully reconstructed SS-OCT images from 2-fold undersampled data (640 points) with high fidelity to full data (1280 points) in 0.59 ms.
  • Spatial aliasing artifacts were effectively removed.
  • The framework showed potential for 3x undersampling with minor quality degradation and improved performance with the optimized undersampling method.

Conclusions:

  • Deep learning enables efficient SS-OCT image reconstruction from significantly undersampled spectral data, increasing imaging speed.
  • The proposed framework integrates seamlessly with existing OCT systems without hardware modifications.
  • This approach offers broad applicability across spectral-domain OCT systems, enhancing speed without compromising resolution or signal-to-noise ratio.