April 2010
Volume 51, Issue 13
Free
ARVO Annual Meeting Abstract  |   April 2010
Iowa Membrane Maps: SD OCT Guided Therapy for Epiretinal Membrane
Author Affiliations & Notes
  • V. B. Mahajan
    Department of Ophthalmology and Visual Sciences,
    Omics Laboratory,
    University of Iowa, Iowa City, Iowa
  • J. C. Folk
    Department of Ophthalmology and Visual Sciences,
    University of Iowa, Iowa City, Iowa
  • S. R. Russell
    Department of Ophthalmology and Visual Sciences,
    University of Iowa, Iowa City, Iowa
  • H. C. Boldt
    Department of Ophthalmology and Visual Sciences,
    University of Iowa, Iowa City, Iowa
  • E. M. Stone
    Department of Ophthalmology and Visual Sciences,
    University of Iowa, Iowa City, Iowa
  • K. Lee
    Department of Electrical and Computer Engineering,
    University of Iowa, Iowa City, Iowa
  • M. D. Abràmoff
    Department of Ophthalmology and Visual Sciences,
    Department of Electrical and Computer Engineering,
    University of Iowa, Iowa City, Iowa
  • Footnotes
    Commercial Relationships  V.B. Mahajan, None; J.C. Folk, None; S.R. Russell, None; H.C. Boldt, None; E.M. Stone, None; K. Lee, None; M.D. Abràmoff, Michael Abramoff, P.
  • Footnotes
    Support  National Eye Institute (R01 EY017066, EB004640), Research to Prevent Blindness, NY, Department for Veterans Affairs.
Investigative Ophthalmology & Visual Science April 2010, Vol.51, 3604. doi:
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      V. B. Mahajan, J. C. Folk, S. R. Russell, H. C. Boldt, E. M. Stone, K. Lee, M. D. Abràmoff; Iowa Membrane Maps: SD OCT Guided Therapy for Epiretinal Membrane. Invest. Ophthalmol. Vis. Sci. 2010;51(13):3604.

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

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Abstract
 
Purpose:
 

The surgical anatomy of macular epiretinal membranes (ERMs) is variable and difficult to visualize. Therefore it can be challenging to create a surgical plane that removes the membrane without damaging the underlying retina. We tested an automated method to identify the optimal surgical plane using Spectral Domain (SD) OCT.

 
Methods:
 

15 eyes with ERMs (9 idiopathic, 6 secondary to retinal detachment, uveitis, or diabetes) underwent automated analysis using our algorithm to segment Sub-ERM spaces. ERM images were registered to the fundus image, and sub-ERM volumes were displayed on a 3D Membrane Map using false color to project the depth of space. During vitrectomy, forceps were used to grasp the membrane over the predicted largest sub-ERM volumes and initiate the peel.

 
Results:
 

Membrane Maps correctly predicted a sub-ERM space and surgical plane in all cases. In 13 eyes, the first grasp succeeded in peeling the entire ERM in a single sheet. In 2 eyes with ERM-retina adhesions, a second grasp was required. Areas without sub-ERM spaces were predictive of greater ERM-retina adhesion. OCT confirmed complete ERM removal in all cases scanned, and visual outcomes were similar to previous reports. There were no intraoperative or other complications during the 3-months follow up. Figure 1. Automated Sub-ERM space segmentation and 3-dimensional Iowa Membrane Map showing largest sub-ERM volume.

 
Conclusions:
 

Iowa Membrane Maps allow objective surgical planning for ERM removal. This technique has the potential for greater safety and surgical predictability.  

 
Keywords: vitreoretinal surgery • imaging/image analysis: clinical • imaging methods (CT, FA, ICG, MRI, OCT, RTA, SLO, ultrasound) 
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