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Quantifying Variability in Longitudinal Peripapillary RNFL and Choroidal Layer Thickness Using Surface Based
Sieun Lee1, Morgan Heisler1, Paul J Mackenzie2
1School of Engineering Science, Simon Fraser University, Burnaby, BC, Canada.
Translational Vision Science & Technology
|March 10, 2017
Summary
This study developed a novel method to precisely measure changes in retinal and choroidal thickness over time using optical coherence tomography. The technique accurately tracks variations, identifying blood vessels as key contributors to retinal nerve fiber layer variability.
Area of Science:
- Ophthalmology
- Medical Imaging
- Biomedical Engineering
Background:
- Longitudinal studies require precise measurement of retinal layers.
- Assessing within-subject variability is crucial for detecting subtle changes.
- Optical coherence tomography (OCT) is a key imaging modality.
Purpose of the Study:
- To evaluate the variability of retinal nerve fiber layer (RNFL) and choroidal thickness using longitudinal OCT data.
- To compare point-to-point measurements with nonrigid surface registration.
- To establish a reliable method for tracking localized changes in retinal layers.
Main Methods:
- Acquired nine repeat peripapillary OCT images from 12 eyes over 3 weeks.
- Segmented RNFL, choroid, and Bruch's membrane opening (BMO).
- Employed nonrigid registration algorithms for point-wise surface mapping and variability analysis using time-standard deviation (tSD).
Main Results:
- Achieved high repeatability for BMO area (ICC=0.993) and eccentricity (ICC=0.972).
- Point-wise tSD for RNFL and choroidal thickness was generally below 12 μm.
- RNFL thickness variability was linked to retinal vessel locations; choroidal thickness showed greater variability than RNFL.
Conclusions:
- Developed a registration-based pipeline for accurate point-wise correspondence of retinal and choroidal surfaces.
- Identified retinal blood vessels as a primary source of RNFL thickness variability.
- The method enables sensitive detection of longitudinal changes with high spatial resolution.
