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Frequency-aware optical coherence tomography image super-resolution via conditional generative adversarial neural

Xueshen Li1, Zhenxing Dong2, Hongshan Liu1

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This study introduces a frequency-aware super-resolution framework for Optical Coherence Tomography (OCT) imaging. The novel approach enhances morphological detail resolution by addressing frequency bias in deep learning reconstruction.

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Area of Science:

  • Medical Imaging
  • Deep Learning
  • Image Reconstruction

Background:

  • Optical Coherence Tomography (OCT) is vital for medical diagnosis and treatment.
  • Deep learning super-resolution enhances OCT image morphological structure resolution.
  • Existing methods neglect frequency fidelity, causing reconstruction bias.

Purpose of the Study:

  • To develop a frequency-aware super-resolution framework for OCT.
  • To overcome the frequency bias in current deep learning reconstruction methods.
  • To improve the resolution of morphological details in medical images.

Main Methods:

  • Proposed a frequency-aware super-resolution framework integrating frequency transformation, skip connection, and alignment modules.
  • Utilized a conditional generative adversarial network (cGAN) architecture.
  • Incorporated a frequency-based loss function.

Main Results:

  • Demonstrated superior performance over existing deep learning frameworks on a coronary OCT dataset.
  • Validated generalizability on fish corneal and rat retinal images.
  • Successfully super-resolved fine morphological details in various OCT applications.

Conclusions:

  • The proposed frequency-aware framework significantly improves OCT image super-resolution.
  • This method effectively addresses frequency bias in deep learning reconstruction.
  • The framework shows broad applicability in medical imaging, particularly for eye imaging and cardiology.