April 2011
Volume 52, Issue 14
Free
ARVO Annual Meeting Abstract  |   April 2011
RNFL Measures with Spectralis vs Cirrus Spectral Domain Optical Coherence Tomography
Author Affiliations & Notes
  • Nimesh B. Patel
    College of Optometry, University of Houston, Houston, Texas
  • Joe L. Wheat
    College of Optometry, University of Houston, Houston, Texas
  • Aldon Rodriguez
    School of Optometry, University of California, Berkeley, Berkeley, California
  • Victoria N. Tran
    Southern California College of Optometry, Fullerton, California
  • Ronald S. Harwerth
    College of Optometry, University of Houston, Houston, Texas
  • Footnotes
    Commercial Relationships  Nimesh B. Patel, None; Joe L. Wheat, None; Aldon Rodriguez, None; Victoria N. Tran, None; Ronald S. Harwerth, None
  • Footnotes
    Support  R01 EY001139, NIH K23 EY018329, NIH P30 EY007551, NIH T35 EY007088
Investigative Ophthalmology & Visual Science April 2011, Vol.52, 173. doi:
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      Nimesh B. Patel, Joe L. Wheat, Aldon Rodriguez, Victoria N. Tran, Ronald S. Harwerth; RNFL Measures with Spectralis vs Cirrus Spectral Domain Optical Coherence Tomography. Invest. Ophthalmol. Vis. Sci. 2011;52(14):173.

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

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Abstract

Purpose: : Measurement of the parapapillary retinal nerve fiber layer (RNFL) thickness provides important information for clinical evaluation of the optic nerve. The RNFL can be imaged and quantified in vivo using spectral domain optical coherence tomography (SD-OCT). Although there are several commercially available SD-OCT instruments, the thickness measures cannot be used interchangeably. The purpose of this study was to determine the influence of scan centration, ocular magnification and segmentation on the agreement of RNFL measures with two commonly used SD-OCT instruments.

Methods: : Standard RNFL scans were acquired using the Cirrus HD-OCT and Spectralis HRA+OCT. Scans from 53 eyes (30 subjects) with no motion artifacts and good image quality were used for data analysis. Agreement between the two instruments was determined for RNFL thickness and area measures using the manufactor's algorithm for segmentation and a custom segmentation algorithm (MATLAB). The custom software included IOL Master ocular biometry measures to compute the transverse scaling for each eye and allowed compensation for major retinal vasculature.

Results: : The instrument derived global RNFL thickness measures from the two instruments correlated well (R2 = 0.81, p<0.01), but with a significant difference between instruments (mean of 7.2 µm; 95% limits of agreement of 15.7 µm to -1.3 µm, ICC = 0.64). With custom RNFL segmentation, the mean difference was decreased to -0.6 µm (95% limits of agreement 5.8 µm to -7.1 µm, ICC = 0.91). Global RNFL thicknesses were related to axial length (R2 = 0.18, p<0.01), while global RNFL area measures were not (R2 = 0.02, p = 0.1). Major retinal vasculature accounted for 11.1±1.5% (Cirrus) or 11.4±1.9% (Spectralis) of the RNFL thickness/area measures.

Conclusions: : The principal source of disagreement in RNFL measures between the Cirrus and Spectralis instruments is the retinal layer segmentation algorithm. In normal eyes, the major retinal vasculature accounts for a significant percentage of the RNFL and is similar between instruments. With incorporation of an individual's ocular biometry, RNFL area measures are independent of axial length. These results are important for the assessment of glaucomatous neuropathy and comparing results across instruments.

Keywords: nerve fiber layer • image processing • optic nerve 
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