June 2013
Volume 54, Issue 15
Free
ARVO Annual Meeting Abstract  |   June 2013
Signal Normalization Between Non-Averaged and Frame-Averaged Optical Coherence Tomography (OCT) Images
Author Affiliations & Notes
  • Chieh-Li Chen
    UPMC Eye Center, Eye and Ear Institute, Ophthalmology and Visual Science Research Center, Department of Ophthalmology, University of Pittsburgh School of Medicine, Pittsburgh, PA
    Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA
  • Hiroshi Ishikawa
    UPMC Eye Center, Eye and Ear Institute, Ophthalmology and Visual Science Research Center, Department of Ophthalmology, University of Pittsburgh School of Medicine, Pittsburgh, PA
    Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA
  • Gadi Wollstein
    UPMC Eye Center, Eye and Ear Institute, Ophthalmology and Visual Science Research Center, Department of Ophthalmology, University of Pittsburgh School of Medicine, Pittsburgh, PA
  • Richard Bilonick
    UPMC Eye Center, Eye and Ear Institute, Ophthalmology and Visual Science Research Center, Department of Ophthalmology, University of Pittsburgh School of Medicine, Pittsburgh, PA
    Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA
  • Ian Sigal
    UPMC Eye Center, Eye and Ear Institute, Ophthalmology and Visual Science Research Center, Department of Ophthalmology, University of Pittsburgh School of Medicine, Pittsburgh, PA
    Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA
  • Larry Kagemann
    UPMC Eye Center, Eye and Ear Institute, Ophthalmology and Visual Science Research Center, Department of Ophthalmology, University of Pittsburgh School of Medicine, Pittsburgh, PA
    Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA
  • James Fujimoto
    Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA
  • Joel Schuman
    UPMC Eye Center, Eye and Ear Institute, Ophthalmology and Visual Science Research Center, Department of Ophthalmology, University of Pittsburgh School of Medicine, Pittsburgh, PA
    Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA
Investigative Ophthalmology & Visual Science June 2013, Vol.54, 5535. doi:
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    • Get Citation

      Chieh-Li Chen, Hiroshi Ishikawa, Gadi Wollstein, Richard Bilonick, Ian Sigal, Larry Kagemann, James Fujimoto, Joel Schuman; Signal Normalization Between Non-Averaged and Frame-Averaged Optical Coherence Tomography (OCT) Images. Invest. Ophthalmol. Vis. Sci. 2013;54(15):5535.

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

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Abstract
 
Purpose
 

Last year we showed that a signal normalization method successfully reduced systematic differences in OCT measurements between 2 spectral domain (SD-) OCT devices, where both provided non-averaged images. However, it turned out to be ineffective between non-averaged and frame-averaged images. The purpose of this study was to modify the signal normalization method by employing virtual signal averaging to make it applicable to reduce the systematic differences between non-averaged and frame-averaged images.

 
Methods
 

The right eyes of 13 healthy subjects were scanned with 2 SD-OCT devices at macular and optic disc regions on the same day (Cirrus HD-OCT (non-averaged); Carl Zeiss Meditec, Dublin, CA; Optic Disc and Macula Cube 200x200, and Spectralis (active tracking and frame-averaged); Heidelberg Engineering, Heidelberg, Germany; macular 20°x20° volume scan (193 sections, 9-frame averaged) and circular RNFL scan (100-frame averaged)). After applying custom virtual signal averaging to Cirrus data, both Cirrus and Spectralis data were normalized with amplitude normalization and z-scaling and sampling density normalization (496 pixels in 1.9 mm became 1024 pixels in 2.0 mm, only applied to Spectralis data). Then sectoral total retinal (TR) thicknesses in the macular region and global mean circumpapillary retinal nerve fiber layer (cpRNFL) thicknesses were automatically measured using software of our own design and their differences between devices were compared to the ones between original device outputs.

 
Results
 

For macular RT, significant systematic differences were detected in all sectors before normalization (Table 1), while no significant difference was found after normalization except for outer temporal and outer nasal sectors (Table 2). Normalization significantly reduced the absolute differences between the devices in all sectors except center (mean absolute difference 20.3 μm [devices] to 6.7 μm [normalized], p<0.0001, paired t-test). For cpRNFL, differences were significant between device outputs (93.6 [Cirrus] vs. 96.7 μm [Spectralis], p=0.0008) but not significant after normalization (102.9 vs. 101.7 μm, p=0.38).

 
Conclusions
 

The reported signal normalization method successfully made clinical measurements from non-averaged and frame-averaged OCT data directly comparable, which may broaden the use of OCT technology in both research and clinical applications.

  
Keywords: 549 image processing • 552 imaging methods (CT, FA, ICG, MRI, OCT, RTA, SLO, ultrasound) • 550 imaging/image analysis: clinical  
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