June 2020
Volume 61, Issue 7
Free
ARVO Annual Meeting Abstract  |   June 2020
Robotically Aligned OCT Imaging with Clinically Relevant Fields of View on the Retina
Author Affiliations & Notes
  • Ryan P McNabb
    Ophthalmology, Duke University Medical Center, Durham, North Carolina, United States
  • Pablo Ortiz
    Biomedical Engineering, Duke University, Durham, North Carolina, United States
  • Mark Draelos
    Biomedical Engineering, Duke University, Durham, North Carolina, United States
  • Joseph A Izatt
    Biomedical Engineering, Duke University, Durham, North Carolina, United States
    Ophthalmology, Duke University Medical Center, Durham, North Carolina, United States
  • Anthony N Kuo
    Ophthalmology, Duke University Medical Center, Durham, North Carolina, United States
    Biomedical Engineering, Duke University, Durham, North Carolina, United States
  • Footnotes
    Commercial Relationships   Ryan McNabb, Johnson & Johnson Vision Care (F), Leica Microsystems (P); Pablo Ortiz, None; Mark Draelos, None; Joseph Izatt, Carl Zeiss Meditec (P), Carl Zeiss Meditec (R), Leica Microsystems (P), Leica Microsystems (R), St. Jude Medical (P), St. Jude Medical (R); Anthony Kuo, Leica Microsystems (P)
  • Footnotes
    Support  NIH R01-EY029302, NIH U01-EY028079
Investigative Ophthalmology & Visual Science June 2020, Vol.61, 3252. doi:
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    • Get Citation

      Ryan P McNabb, Pablo Ortiz, Mark Draelos, Joseph A Izatt, Anthony N Kuo; Robotically Aligned OCT Imaging with Clinically Relevant Fields of View on the Retina. Invest. Ophthalmol. Vis. Sci. 2020;61(7):3252.

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

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Abstract

Purpose : OCT is a ubiquitous ophthalmic imaging technology both in research and clinical eye care. However, as a point scanning technique, OCT is very sensitive to subject/operator motion, and it can be challenging, particularly for those with insufficient training, to operate and acquire images. We previously developed a robotically tracking and aligning OCT system [1] that automatically acquired OCT volumes with ~10° field-of-view (FOV). Robot payload considerations and working distance safety restrictions placed constraints on the sample arm design and FOV. We now report development of a light weight sample arm that provides retinal FOV large enough to encompass the optic nerve head (ONH) and fovea during the automatic acquisition while maintaining a safe distance to the eye.
[1] M Draelos, AN Kuo, JA Izatt, 2019. Robotically Aligned OCT Scanner for Automated Patient Tracking Retinal Imaging. IOVS, 60(9), pp.1268-1268.

Methods : We developed a custom robotically mounted retinal OCT system with a swept source (λ0=1043nm±72nm; 100 kHz) providing 30° FOV on the retina. To achieve a clinically relevant FOV while maintaining a safe distance from the patient (86mm), large diameter (70mm) air-spaced achromatic lenses were used for imaging. To maintain a low mass for the robot (2.08kg), the sample arm was developed primarily with 3D printing and carbon fiber. A consented volunteer was imaged with the described system at the Duke Eye Center under an IRB approved protocol.

Results : Under ambient lighting and in a seated position free of any restraints (no chin rest or forehead strap, Fig. 1), the system automatically aligned on the subject’s eye allowing for motion compensated OCT B-scans and volumes (5 sec/scan) of the retina. A B-scan, summed volume projection (SVP), and volume rendering from these acquisitions are shown in Fig. 2.

Conclusions : We have demonstrated a robotically aligned OCT that automatically acquires volumes containing both the optic nerve and fovea. This has the potential to remove the need for trained personnel to operate OCT systems so that personnel only need to assess the images for diagnostic information.

This is a 2020 ARVO Annual Meeting abstract.

 

A) Render of robot mounted OCT system B) Robotic OCT system at Duke Eye Center

A) Render of robot mounted OCT system B) Robotic OCT system at Duke Eye Center

 

A) Averaged B-scan with fovea, ONH, and resolved ELM (blue). B) 30° FOV SVP with fovea (red) and ONH. C) Volume rendering with fovea (red) and ONH (yellow). Residual axial motion (<100µm) was filtered from volume.

A) Averaged B-scan with fovea, ONH, and resolved ELM (blue). B) 30° FOV SVP with fovea (red) and ONH. C) Volume rendering with fovea (red) and ONH (yellow). Residual axial motion (<100µm) was filtered from volume.

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