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Complex conjugate removal in optical coherence tomography using phase aware generative adversarial network.

Valentina Bellemo1,2,3, Richard Haindl4, Manojit Pramanik5

  • 1Nanyang Technological University, School of Chemistry, Chemical Engineering and Biotechnology, Singapore, Singapore.

Journal of Biomedical Optics
|February 18, 2025
PubMed
Summary
This summary is machine-generated.

A new deep learning method uses generative adversarial networks to remove complex conjugate artifacts (CCAs) from optical coherence tomography (OCT) scans. This software-based approach eliminates the need for extra hardware, offering a cost-effective solution for enhanced imaging.

Keywords:
complex conjugate removalgenerative adversarial networksoptical coherence tomography

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Area of Science:

  • Biomedical Optics
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Complex conjugate artifacts (CCAs) in frequency-domain optical coherence tomography (FD-OCT) necessitate additional hardware, increasing system complexity and cost.
  • A software-based solution for CCA removal is highly desirable for efficiency and cost-effectiveness.

Purpose of the Study:

  • To develop a deep learning approach for effective CCA removal in OCT scans.
  • To eliminate the need for extra hardware components in FD-OCT systems.

Main Methods:

  • Implementation of a deep learning method utilizing generative adversarial networks (GANs).
  • Leveraging both intensity and phase images from OCT scans to improve artifact removal.
  • Development of a CCA removal-GAN model.

Main Results:

  • Successful conversion of OCT scans with CCAs to artifact-free scans across diverse samples (phantoms, human skin, mouse eyes).
  • Demonstration of in vivo imaging using a phase-stable swept source-OCT prototype.
  • Significant performance enhancement in CCA removal through the integration of phase images.

Conclusions:

  • The developed method offers a low-cost, data-driven, software-based solution for CCA removal.
  • Enhancement of FD-OCT imaging capabilities through effective artifact reduction.
  • Provides a viable alternative to hardware-based CCA removal techniques.