May 2007
Volume 48, Issue 13
Free
ARVO Annual Meeting Abstract  |   May 2007
OCT Fundus Imaging for Enhanced Visualization of Retinal Pathologies
Author Affiliations & Notes
  • J. J. Liu
    Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts
  • I. Gorczynska
    Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts
    New England Eye Center, Tufts-New England Medical Center, Boston, Massachusetts
  • V. J. Srinivasan
    Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts
  • R. W. Chen
    New England Eye Center, Tufts-New England Medical Center, Boston, Massachusetts
  • E. Reichel
    New England Eye Center, Tufts-New England Medical Center, Boston, Massachusetts
  • J. S. Duker
    New England Eye Center, Tufts-New England Medical Center, Boston, Massachusetts
  • M. Wojtkowski
    Institute of Physics, Nicolaus Copernicus University, Torun, Poland
  • J. G. Fujimoto
    Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts
  • Footnotes
    Commercial Relationships J.J. Liu, None; I. Gorczynska, None; V.J. Srinivasan, None; R.W. Chen, None; E. Reichel, None; J.S. Duker, None; M. Wojtkowski, None; J.G. Fujimoto, Optovue, F; Carl Zeiss Meditec, Inc., P.
  • Footnotes
    Support NIH RO1-EY11289-20, NSF BES-0522845, AFOSR FA9550-040-1-0046, AFOSR FA9550-040-1-0011
Investigative Ophthalmology & Visual Science May 2007, Vol.48, 3443. doi:
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    • Get Citation

      J. J. Liu, I. Gorczynska, V. J. Srinivasan, R. W. Chen, E. Reichel, J. S. Duker, M. Wojtkowski, J. G. Fujimoto; OCT Fundus Imaging for Enhanced Visualization of Retinal Pathologies. Invest. Ophthalmol. Vis. Sci. 2007;48(13):3443.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose:: Recent advances enable high-speed OCT retinal imaging. Spectral/Fourier domain OCT enables data acquisition rates of ~25,000 axial scans per second. Such speeds enable three-dimensional OCT (3D-OCT) imaging using dense raster scanning. 3D-OCT imaging provides comprehensive coverage of the retina enabling visualization of focal pathologies and precise registration of OCT images with fundus features. However, detailed analysis of all individual OCT images in a 3D-OCT data set is unrealistic in clinical practice. En face visualization of 3D-OCT data including thickness maps and layered fundus images is investigated as an approach to solve this problem and improve visualization.

Methods:: High-speed, ultrahigh resolution OCT using spectral/Fourier domain detection was performed using a research prototype instrument with 3.5 µm axial resolution. Dense raster scanning was performed over a 6x6 mm of the macula, yielding 180x512x1024 voxels of 3D-OCT data. En face OCT fundus images, sectioned at different levels of the retina, as well as thickness maps were created using a segmentation algorithm based on Laplacian edge detection. Correction for retinal curvature was performed to enable reliable en face sectioning. En face images were generated either by summation of signals from selected retinal layers or weighted axial summation of the entire 3D-OCT data. The latter method preserves information about all retinal layers, while enhancing the information from chosen sections of the retina. Sectioning was applied to different retinal layers to improve visualization of pathologies. Results were compared to elevation maps.

Results:: OCT fundus images provide maps showing changes in the light scattering of the retinal layers related to different pathological conditions. For example: displays of pigmentary changes in the RPE, different types of deposits, photoreceptor and/or RPE atrophy can reveal tissue microstructure and give insight into lesion morphology.

Conclusions:: OCT fundus imaging, sectioning and segmentation methods may be optimized for specific diseases affecting localized regions or layers of the retina. Displaying these findings relative to retinal features such as vasculature, enables quick and intuitive assessment of the locus of lesions and may help improve diagnostic efficiency.

Keywords: imaging methods (CT, FA, ICG, MRI, OCT, RTA, SLO, ultrasound) • image processing • retina 
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