May 2006
Volume 47, Issue 13
ARVO Annual Meeting Abstract  |   May 2006
Imaging Diabetic Retinopathy With Near Infrared Light And Polarimetry
Author Affiliations & Notes
  • A.E. Elsner
    Optometry, Indiana University, Bloomington, IN
  • D.A. VanNasdale
    Optometry, Indiana University, Bloomington, IN
  • A. Weber
    University Eye Hospital, RWTH, Aachen, Germany
  • M. Miura
    Ophthalmology, Tokyo Medical University, Tokyo, Japan
  • Footnotes
    Commercial Relationships  A.E. Elsner, Indiana University, P; D.A. VanNasdale, None; A. Weber, None; M. Miura, None.
  • Footnotes
    Support  NIH grants EB002346, EY007624
Investigative Ophthalmology & Visual Science May 2006, Vol.47, 4062. doi:
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      A.E. Elsner, D.A. VanNasdale, A. Weber, M. Miura; Imaging Diabetic Retinopathy With Near Infrared Light And Polarimetry . Invest. Ophthalmol. Vis. Sci. 2006;47(13):4062.

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

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Purpose: : To develop a low cost method for screening for diabetic retinopathy, including not only early features but also signs that require immediate referral by ETDRS or International Classification Criteria. To determine whether features of diabetic retinopathy can be detected using only nonmydriatic, near infrared imaging, without high resolution and short wavelength light, problematic with dark fundi or media problems.

Methods: : We used confocal scanning laser polarimetry (GDx, LDT/CZM), to digitize a series of 780 nm, macular images of 256 x 256 pixels and 15 deg, in less than 1 sec. There were 40 raw images: 20 input polarizations and 2 detectors, one parallel to the input polarization and one perpendicular (crossed). From the raw images, we computed 18 images differing in polarization content. The birefringence image is the amplitude of modulation pixel by pixel of the crossed detector. The crossed and parallel phase images map which input polarization angle had the maximum amplitude. The remaining, or unmodulated light, produces the depolarized light image. The average image is the average of both detectors. The maximum of parallel polarized light image is the greatest amplitude over all input polarizations. We computed Michelson contrast, C = (Lon – Loff)/(Lon + Loff), where Lon is the grayscale on a feature and Loff is the grayscale adjacent to the feature.

Results: : Many features of diabetic retinopathy were readily detected with near infrared imaging: microaneurysms validated by fluorerscein angiography, microhemorrhages, intraretinal hemorrhages, venous beading, vessel loops, intraretinal microvascular abnormalities, macular edema, hard exudates, and traction. Different image types emphasized different features. Images with more polarization content emphasized edema. Depolarized light images had high contrast of vessel abnormalities, backlit by deeper layers. The Michelson contrast of a vessel loop was –.47 for the depolarized light image, but –.19 and –.20 for the average image and maximum of the parallel polarized light detector, respectively.

Conclusions: : Features of diabetic retinopathy were readily detected using high contrast, nonmydriatic, near infrared imaging.

Keywords: diabetic retinopathy • clinical (human) or epidemiologic studies: systems/equipment/techniques • neovascularization 

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