April 2014
Volume 55, Issue 13
Free
ARVO Annual Meeting Abstract  |   April 2014
Automated Thickness Correction Model for Tilted SDOCT Volumes
Author Affiliations & Notes
  • Bhavna Josephine Antony
    School of Optometry and Vision Science Graduate Group, University of California, Berkeley, Berkeley, CA
  • Austin Roorda
    School of Optometry and Vision Science Graduate Group, University of California, Berkeley, Berkeley, CA
  • Paul F Stetson
    Carl Zeiss Meditec Inc., Dublin, CA
  • Brandon J Lujan
    School of Optometry and Vision Science Graduate Group, University of California, Berkeley, Berkeley, CA
    West Coast Retina Medical Group, San Francisco, CA
  • Footnotes
    Commercial Relationships Bhavna Antony, None; Austin Roorda, University of California, Berkeley (P); Paul Stetson, Carl Zeiss Meditec, Inc. (E); Brandon Lujan, University of California, Berkeley (P)
  • Footnotes
    Support None
Investigative Ophthalmology & Visual Science April 2014, Vol.55, 4791. doi:https://doi.org/
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    • Get Citation

      Bhavna Josephine Antony, Austin Roorda, Paul F Stetson, Brandon J Lujan; Automated Thickness Correction Model for Tilted SDOCT Volumes. Invest. Ophthalmol. Vis. Sci. 2014;55(13):4791. doi: https://doi.org/.

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

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Abstract
 
Purpose
 

Off-axis Spectral Domain Optical Coherence Tomography (SDOCT) acquisition can result in tilted images and erroneous thickness measurements. Using scans that were purposefully acquired off-axis, we developed an automated method for the assessment and correction of thickness errors based on angular deviation.

 
Methods
 

At least five volumetric SDOCT scans of the macula were obtained from one eye of 20 normal subjects. One scan was acquired through a pupil position such that the scan appeared “flat” in both the horizontal and vertical dimensions. Subsequent scans were acquired through multiple pupil positions, resulting in an apparent axial “tilt” along either the horizontal or vertical dimension. The inner limiting membrane (ILM) and the retinal pigment epithelium (RPE) were then segmented using an automated approach. The angular deviation from the “flat” optical axis was assessed by fitting a plane to the RPE surface. The multiple volumes were registered to each other and the average total retinal thickness was evaluated in 9 sectors (Fig. 1), where the diameters of the annuli are 1mm, 3mm and 5mm. The increase in thickness seen in the tilted volumes was modeled using a regression model that incorporated the thickness measurements as well as the angular deviation. The model was built using the measurements obtained from 10 subjects (5 OD, 5 OS) and subsequently tested using the volumes obtained from the remaining 10 subjects.

 
Results
 

The mean thickness error (Fig. 1A) noted between the off-axis scans and the correctly acquired “flat” scans was 7.91 +/- 4.31 µm. The linear regression model developed showed an adjusted R-square value of 0.986, and reduced the error (Fig. 1B) to 0.49 +/- 4.07 µm (p < 0.001). The model indicated a strong relationship between the cosine of the angular deviation and the increased thickness (p < 0.001).

 
Conclusions
 

The regression model developed was able to effectively reduce errors in thickness measurements derived from scans that were acquired off-axis. In large clinical trials using volumetric SDOCT, the improved accuracy possible using this method may lead to more reliable clinical end points.

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