June 2013
Volume 54, Issue 15
ARVO Annual Meeting Abstract  |   June 2013
Stabilized cone imaging with adaptive optics optical coherence tomography
Author Affiliations & Notes
  • Omer Kocaoglu
    School of Optometry, Indiana University, Bloomington, IN
  • R. Ferguson
    Physical Sciences Inc., Andover, MA
  • Zhuolin Liu
    School of Optometry, Indiana University, Bloomington, IN
  • Ravi Jonnal
    School of Optometry, Indiana University, Bloomington, IN
  • Qiang Wang
    School of Optometry, Indiana University, Bloomington, IN
  • Daniel Hammer
    Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, MD
  • Donald Miller
    School of Optometry, Indiana University, Bloomington, IN
  • Footnotes
    Commercial Relationships Omer Kocaoglu, None; R. Ferguson, Physical Sciences Inc (E), Physical Sciences Inc (P); Zhuolin Liu, None; Ravi Jonnal, 7,364,296 (P); Qiang Wang, None; Daniel Hammer, Physical Sciences Inc. (C), Physical Sciences Inc. (P); Donald Miller, n/a (P)
  • Footnotes
    Support None
Investigative Ophthalmology & Visual Science June 2013, Vol.54, 5543. doi:
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      Omer Kocaoglu, R. Ferguson, Zhuolin Liu, Ravi Jonnal, Qiang Wang, Daniel Hammer, Donald Miller; Stabilized cone imaging with adaptive optics optical coherence tomography. Invest. Ophthalmol. Vis. Sci. 2013;54(15):5543.

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

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Purpose: Cone photoreceptors exhibit a complex optical signature that has become increasingly valuable in ophthalmologic examination. Adaptive optics optical coherence tomography (AO-OCT) is a highly sensitive method that is particularly well suited for 3D imaging the cone signature. Like all in vivo retinal imaging methods, however, AO-OCT suffers from the effects of involuntary eye movements that blur and distort the image, especially at the cone level. Compensation of movement can be realized with registration algorithms in post processing or dynamic retinal tracking in hardware. Tracking is particularly attractive for AO-OCT as it can compensate for eye motion larger than the imaging field of view and permits repeated imaging of the same retinal patch. In light of these advantages, this study examines the efficacy of an active retinal tracker integrated into AO-OCT for cone imaging.

Methods: The 2nd-generation Indiana AO-OCT system was integrated with a customized retina tracker module. Key AO-OCT components included a broadband light source (λc=800nm, Δλ=160nm, bandpassed to Δλ=80nm), an astigmatism-free sample arm, and a high-speed linescan camera operating at 100K Alines/s. The tracker module was designed to stabilize the location of the AO-OCT beam relative to a specific retinal landmark tracked at 1060nm, and to provide a 35° field of view SLO image at 950 nm. Closed-loop tracking performance was assessed by comparing cone motion in 1.8°x1.8° volume videos acquired with and without dynamic retinal tracking. Videos were obtained at 4° nasal and 3° superior to the fovea of a normal subject (30 yrs). Five sessions of volume videos were collected, each consisting of five volumes (1 vol/s). En face projections of the AO-OCT volumes were assessed for lateral cone motion. To avoid masking tracker performance, no post-processing registration was applied to the videos.

Results: The average maximum cone displacement was 18±5μm with tracking compared to 150±107μm without tracking, a factor of eight improvement. The corresponding contrast improvement in the averaged en face cone image was 38% (p=0.008). Tracking was sufficiently robust to find repeatedly the same cone patch across sessions.

Conclusions: Dynamic tracking stabilized AO-OCT cone images, sufficiently so for monitoring the same cones over time.

Keywords: 648 photoreceptors • 552 imaging methods (CT, FA, ICG, MRI, OCT, RTA, SLO, ultrasound) • 688 retina  

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