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Denoising OCT videos based on temporal redundancy.

Emmanuelle Richer1,2, Marissé Masís Solano2,3, Farida Cheriet1

  • 1Department of Computer Engineering and Software Engineering, École Polytechnique de Montréal, Montreal, QC, H3T 1J4, Canada.

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This study introduces a novel one-cycle denoising method for Optical Coherence Tomography (OCT) imaging. It significantly enhances image clarity by synchronizing with cardiac pulse, improving eye disease diagnosis.

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Area of Science:

  • Ophthalmology
  • Biomedical Imaging
  • Medical Technology

Background:

  • Speckle noise in Optical Coherence Tomography (OCT) B-scans degrades image quality, hindering accurate eye disease identification and progression monitoring.
  • Reliable extraction of biomarkers from time-series OCT data is compromised by noise, impacting clinical diagnostics.

Purpose of the Study:

  • To develop and evaluate a novel denoising strategy for OCT imaging by synchronizing with cardiac pulse.
  • To compare the performance of the proposed one-cycle denoising method against deep-learning (Noise2Noise) and classical (BM3D, NLM) techniques.

Main Methods:

  • A one-cycle denoising strategy was implemented by phase-wrapping OCT frames to cardiac pulsation and averaging synchronized frames.
  • The denoising performance was systematically assessed using image quality descriptors and region-specific metrics on ocular anatomy.
  • Comparative analysis included Noise2Noise, BM3D, and Non-Local Means (NLM) algorithms.

Main Results:

  • The one-cycle denoising method demonstrated superior performance compared to Noise2Noise, BM3D, and NLM.
  • The strategy significantly improved OCT image quality and preserved high-resolution structures within eye tissues.
  • Enhanced image clarity facilitates more reliable biomarker extraction for time-series analysis.

Conclusions:

  • The proposed cardiac-synchronized one-cycle denoising method offers a robust solution for improving OCT image quality.
  • This technique enhances the reliability of quantitative OCT analysis for eye disease diagnosis and progression monitoring.
  • The workflow is readily implementable in clinical settings, offering practical benefits for ophthalmological imaging.