June 2015
Volume 56, Issue 7
Free
ARVO Annual Meeting Abstract  |   June 2015
Perifoveal Capillary Perfusion Pressure and Wall Shear Stress Estimated by a Computational Model Based on Adaptive Optics Scanning Laser Ophthalmoscopy (AOSLO)
Author Affiliations & Notes
  • Yang Lu
    Beetham Eye Institute, Joslin Diabetes Center, Boston, MA
  • Miguel Bernabeu
    Centre for Computational Science, University College London, London, United Kingdom
    CoMPLEX, University College London, London, United Kingdom
  • Jan Lammer
    Beetham Eye Institute, Joslin Diabetes Center, Boston, MA
    Department of Ophthalmology and Optometry, Medical University Vienna, Vienna, Austria
  • Charles Chen Cai
    Beetham Eye Institute, Joslin Diabetes Center, Boston, MA
  • Lloyd Paul Aiello
    Beetham Eye Institute, Joslin Diabetes Center, Boston, MA
    Department of Ophthalmology, Harvard Medical School, Boston, MA
  • Jennifer K Sun
    Beetham Eye Institute, Joslin Diabetes Center, Boston, MA
    Department of Ophthalmology, Harvard Medical School, Boston, MA
  • Footnotes
    Commercial Relationships Yang Lu, None; Miguel Bernabeu, None; Jan Lammer, None; Charles Cai, None; Lloyd Paul Aiello, None; Jennifer Sun, Boston Micromachines (F), Genentech (F), Kowa (C), Novartis (C), Optovue (F), Regeneron (C)
  • Footnotes
    Support None
Investigative Ophthalmology & Visual Science June 2015, Vol.56, 5665. doi:
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      Yang Lu, Miguel Bernabeu, Jan Lammer, Charles Chen Cai, Lloyd Paul Aiello, Jennifer K Sun; Perifoveal Capillary Perfusion Pressure and Wall Shear Stress Estimated by a Computational Model Based on Adaptive Optics Scanning Laser Ophthalmoscopy (AOSLO). Invest. Ophthalmol. Vis. Sci. 2015;56(7 ):5665.

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

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Abstract
 
Purpose
 

To estimate capillary perfusion pressure (CPP) and wall shear stress (WSS) in the perifoveal capillaries through advanced computational analysis of AOSLO images from diabetic and non-diabetic individuals.

 
Methods
 

AOSLO multiply scattered light imaging of perifoveal capillary networks was performed for eyes of diabetic and non-diabetic subjects. Standard deviation perfusion maps were generated from 50-frame videos (30 frames/sec, 2°×2° area) and montaged to form 5°×5° images. After Frangi filtering, image thresholding and manual editing by trained graders, a binary mask was generated to construct 3-D geometrical models with arteriolar inlets and venular outlets identified by registration to a 100° color fundus photograph. Mean arterial pressure, IOP and the geometry of the network were utilized in a computational model to estimate CPP (with respect to the venular outlet pressure) and WSS. On average, >600,000 data points were sampled across each vascular network.

 
Results
 

3 non-diabetic eyes (2 subjects) and 2 eyes of 1 subject with mild NPDR (DM duration 19 yrs) had mean±SD age of 33±4 yrs, and 2 were male. Mean estimated CPP was 18.5±11.6 mmHg and WSS was 6.0±5.3 Pa. In the 3 non-diabetic eyes, distribution of data points within the 1st, 2nd, 3rd & 4th quartiles of the average distribution were 21.7, 22.4, 22.2, 33.7% for CPP, and 22.7, 21.9, 27.0, 28.3% for WSS. In the 2 diabetic eyes the values were 24.6, 30.5, 34.0, 10.8%, and 24.4, 30.2, 24.7, 10.7%. On average, CPP and WSS did not differ between the inferior and superior macular quadrants.

 
Conclusions
 

Previous studies of CPP and WSS have not been able to assess human retinal capillaries. Our novel approach leverages the attributes of advanced computational modeling with AOSLO to permit estimation of CPP and WSS in these previously inaccessible vessels of the human eye in vivo. These preliminary data suggest that AOSLO-derived computational blood flow models have potential to evaluate human flow dynamics in the smallest retinal vessels of the retina.  

 
Figure 1. a. Montaged AOSLO perfusion map over fundus photograph. b. Central 5°×5° of the perifoveal capillary network. c. Vessel binary mask after image segmentation. d. CPP map using reconstructed 3D vessel network. e. WSS map for a subset of the vessels in Fig. 1d. Arrows indicate stress direction and color indicates magnitude.
 
Figure 1. a. Montaged AOSLO perfusion map over fundus photograph. b. Central 5°×5° of the perifoveal capillary network. c. Vessel binary mask after image segmentation. d. CPP map using reconstructed 3D vessel network. e. WSS map for a subset of the vessels in Fig. 1d. Arrows indicate stress direction and color indicates magnitude.

 
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