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Related Experiment Video

Updated: Jun 10, 2025

Multimodal Volumetric Retinal Imaging by Oblique Scanning Laser Ophthalmoscopy oSLO and Optical Coherence Tomography OCT
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Intra-operative OCT (iOCT) Super Resolution: a Two-Stage Methodology Leveraging High Quality Pre-operative OCT Scans.

Charalampos Komninos1, Theodoros Pissas1, Blanca Flores2

  • 1School of Biomedical Engineering & Imaging Sciences, King's College London, SE1 7EU, London, UK.

Ophthalmic Medical Image Analysis : 9Th International Workshop, OMIA 2022, Held in Conjunction with MICCAI 2022, Singapore, Singapore, September 22, 2022, Proceedings. OMIA (Workshop) (9Th : 2022 : Singapore, Singapore)
|October 15, 2024
PubMed
Summary

This study enhances low-resolution intra-operative Optical Coherence Tomography (iOCT) images using pre-operative high-resolution OCT (preOCT) data. The novel two-stage method significantly improves image quality for precise retinal therapies.

Keywords:
Image qualityiOCTsuper-resolution

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Area of Science:

  • Ophthalmology
  • Biomedical Engineering
  • Medical Imaging

Background:

  • Regenerative therapies offer potential for sight restoration in degenerative diseases.
  • Precise intra-retinal drug delivery requires high-quality visualization of retinal layers.
  • Intra-operative Optical Coherence Tomography (iOCT) provides real-time but inadequate image quality for such procedures.

Purpose of the Study:

  • To develop a super-resolution methodology for enhancing low-resolution iOCT images.
  • To leverage pre-operative high-resolution OCT (preOCT) images to improve iOCT quality.
  • To enable precise intra-retinal therapy delivery through enhanced iOCT visualization.

Main Methods:

  • A two-stage super-resolution approach was proposed.
  • CycleGAN was used to learn the degradation from high-resolution (HR) to low-resolution (LR) OCT, generating pseudo iOCT images from preOCT data.
  • A Pix2Pix model was trained on pseudo iOCT/preOCT pairs for super-resolution mapping.

Main Results:

  • The proposed method significantly outperforms existing learning-based super-resolution techniques.
  • Quantitative analysis using full-reference and no-reference image quality metrics confirmed superior performance.
  • Achieved iOCT image quality comparable to preOCT quality.

Conclusions:

  • The developed super-resolution technique can enhance iOCT image quality for vitreoretinal surgery.
  • This advancement may facilitate the adoption of iOCT in clinical practice without hardware upgrades.
  • Improved iOCT visualization supports precise delivery in regenerative therapies for vision restoration.