May 2006
Volume 47, Issue 13
ARVO Annual Meeting Abstract  |   May 2006
Snap–Shot Human Retinal Oximetry
Author Affiliations & Notes
  • J.Z. Xie
    Doheny Retina Institute, USC/Doheny, Los Angeles, CA
  • W. Johnson
    Jet Propulsion Laboratory, Pasadena, CA
  • A. Walsh
    Doheny Retina Institute, USC/Doheny, Los Angeles, CA
  • D. Wilson
    Jet Propulsion Laboratory, Pasadena, CA
  • G. Bearman
    Jet Propulsion Laboratory, Pasadena, CA
  • M. Humayun
    Doheny Retina Institute, USC/Doheny, Los Angeles, CA
  • Footnotes
    Commercial Relationships  J.Z. Xie, None; W. Johnson, None; A. Walsh, None; D. Wilson, None; G. Bearman, None; M. Humayun, None.
  • Footnotes
    Support  Reichert inc.
Investigative Ophthalmology & Visual Science May 2006, Vol.47, 3306. doi:
  • Views
  • Share
  • Tools
    • Alerts
      This feature is available to authenticated users only.
      Sign In or Create an Account ×
    • Get Citation

      J.Z. Xie, W. Johnson, A. Walsh, D. Wilson, G. Bearman, M. Humayun; Snap–Shot Human Retinal Oximetry . Invest. Ophthalmol. Vis. Sci. 2006;47(13):3306.

      Download citation file:

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

  • Supplements

Retinal hyperspectral imaging offers the potential to capture in vivo metabolic and physiologic information by identifying unique patterns of fundus reflectance that classify tissues and quantify cellular metabolites. Distinctive hemoglobin spectral signatures can provide potentially both qualitative and quantitative oxygen saturation assessment. Current ophthalmic hyperspectral imaging systems typically required long exposure times and therefore are plagued with motion artifacts from saccades. Snap–shot spectroscopy avoids these potential pitfalls by capturing a full spatial–spectral image cube in a single camera flash.


6 eyes of 3 normal subjects were imaged with a computed tomography ‘snap–shot’ imaging spectrometer (CTIS) that obtains full spectral information for each pixel in a 2–D scene with a single flash. Expectation maximization routines were used to reconstruct the spectral image cube from captured data. Optical density within the blood vessel was then calculated by taking the logarithm of the ratio of perivascular intensity to vascular intensity. These curves were compared to simulated oxy–deoxyhemoglobin reflectance curves with Pearson Product Moment Correlation Coefficients analysis.


Pseudocolor oxygen–saturation correlation maps representing tissue oxygenation across the image were generated by correlating captured spectra with simulated spectra (fig). The average correlation for subgroups of spectra (72 points each) was excellent (r=0.9, p<0.01). Areas of high correlation, however, occurred in patches with large differences existing between adjacent vessel segments. This may be due to lighting variation along the vessel length and will need to be investigated in future studies.


Snap–shot retinal imaging spectroscopy may be a useful and practical tool for both qualitative and quantitative oxygen saturation measurements within retinal vessels. Expansion of this capability to include other clinically–relevant compounds such as lipofuscin and cytochrome aa3 holds promise as a functional imaging modality for use both in clinical trials and in clinical practice.  

Keywords: imaging methods (CT, FA, ICG, MRI, OCT, RTA, SLO, ultrasound) • blood supply • diabetic retinopathy 

This PDF is available to Subscribers Only

Sign in or purchase a subscription to access this content. ×

You must be signed into an individual account to use this feature.