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Using Eye-tracking to Assess the Relative Importance of Visual and Vestibular Input to Subcortical Motion Processing in the Roll Plane
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Involuntary eye motion correction in retinal optical coherence tomography: Hardware or software solution?

Ahmadreza Baghaie1, Zeyun Yu2, Roshan M D'Souza3

  • 1Department of Electrical Engineering, University of Wisconsin-Milwaukee, WI 53211, USA.

Medical Image Analysis
|February 17, 2017
PubMed
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This review covers hardware and software techniques for correcting eye motion artifacts in Optical Coherence Tomography (OCT) imaging. Hardware methods offer superior results by capturing motion data, while software methods are adaptable for older systems.

Area of Science:

  • Ophthalmology
  • Biomedical Imaging
  • Medical Technology

Background:

  • Eye motion during Optical Coherence Tomography (OCT) acquisition introduces artifacts.
  • These artifacts degrade the quality and anatomical accuracy of retinal images.
  • Accurate retinal imaging is crucial for diagnosing and monitoring various eye diseases.

Purpose of the Study:

  • To review and categorize state-of-the-art techniques for correcting eye motion artifacts in OCT imaging.
  • To compare the advantages and limitations of hardware-based versus software-based artifact reduction methods.
  • To provide insights into future research directions in OCT artifact correction.

Main Methods:

  • Categorization of artifact reduction techniques into hardware-based and software-based approaches.
Keywords:
Motion artifact correctionOptical Coherence Tomography (OCT)Retina

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  • Hardware-based methods utilize additional sensors to capture eye motion during OCT data acquisition.
  • Software-based methods approximate motion patterns using reference images or prior assumptions.

    • Main Results:

    • Hardware-based techniques provide superior artifact correction by directly measuring eye motion.
    • Software-based techniques offer moderate success, highly dependent on initial image quality.
    • Software methods are valuable for processing legacy OCT data lacking dedicated motion tracking hardware.

    Conclusions:

    • Hardware-based OCT artifact correction yields higher quality retinal images but faces implementation challenges.
    • Software-based methods are essential for retrospective analysis of OCT data from systems without advanced hardware.
    • Further research is needed to optimize both approaches for robust and widespread clinical application.