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Youngseok Song, Daniel Ruminski, Katie A. Lucy, Gadi Wollstein, JOONGWON SHIN, Kyung Rim Sung, Joel S Schuman, Hiroshi Ishikawa; Averaging Multiple OCT Volumes Improves Visibility of Lamina Cribrosa. Invest. Ophthalmol. Vis. Sci. 2017;58(8):1311.
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© ARVO (1962-2015); The Authors (2016-present)
Imaging the lamina cribrosa (LC) has gained importance in the understanding and assessment of glaucoma. However, its clinical utility is limited because typical optical coherence tomography (OCT) images of the LC are of poor quality which precludes performing reliable micro-structural analysis. The purpose of this study was to assess an image enhancement technique involving the averaging of multiple OCT volumes.
Repetitive OCT volumes (up to 6 volumes scanned on the same day) from 10 healthy eyes (10 subjects) were acquired using Cirrus HD-OCT (Zeiss, Dublin, CA; software version 22.214.171.124; Optic Disc 200x200 scan pattern). All volumes had signal strength of 7 or above. 3D OCT volumes were first registered to each other using the Elastix software, then super-sampled to 800x800x1024 using 3D bi-cubic interpolation. Signal to noise ratio (SNR) and contrast to noise ratio (CNR) were calculated to quantify the image quality of the visible LC. SNR and CNR were then compared between multiple-volume-averaged images and corresponding single volume images using the Wilcoxon test.
Image quality of the visible LC showed notable improvement with multiple volume averaging (Figure 1-6). SNR showed statistically significant improvement from the baseline image quality after 3 or more volumes were averaged (P=0.01), while CNR showed significant improvement from baseline after 2 or more volumes were averaged (P=0.0005) (Figure A, B).
The presented image enhancement technique successfully improved image quality of the visible LC. This technique can be applied to any existing OCT images as long as multiple volumes (minimum of 3 volumes) are available on the same eye from the same session in order to improve image quality.
This is an abstract that was submitted for the 2017 ARVO Annual Meeting, held in Baltimore, MD, May 7-11, 2017.
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