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Area of Science:

  • Ophthalmology
  • Biomedical Imaging
  • Artificial Intelligence

Background:

  • High-resolution in vivo adaptive optics (AO) imaging allows cellular assessment of cone photoreceptors.
  • Dense pixel sampling in AO imaging limits acquisition speed, causing motion artifacts and large datasets.

Purpose of the Study:

  • To introduce an AI-assisted imaging framework to improve AO imaging efficiency.
  • To overcome the tradeoff between pixel resolution and acquisition speed in AO imaging.

Main Methods:

  • Utilized a residual in residual transformer generative adversarial network (RRTGAN) AI method.
  • Developed an AI framework to restore pixel resolution from sparsely sampled AO images.
  • Compared AI-restored images with ground truth and histology data.

Main Results:

  • RRTGAN enabled data-efficient imaging, restoring high-quality images from only 25% of the data.
  • AI-assisted images closely matched ground truth images.
  • Cone spacing estimates from AI-assisted images aligned with histological data.

Conclusions:

  • AI-assisted imaging, specifically RRTGAN, can overcome the pixel sampling and speed tradeoff in AO imaging.
  • This approach enhances the efficiency of routine clinical AO imaging.
  • Demonstrates potential for improving diagnostic capabilities in ophthalmology.