September 2016
Volume 57, Issue 12
Open Access
ARVO Annual Meeting Abstract  |   September 2016
Computational Fluid Dynamics Modeling of Diabetic Retinal Microaneurysms (MAs) Based on Adaptive Optics Scanning Laser Ophthalmoscopy (AOSLO)
Author Affiliations & Notes
  • Yang Lu
    Beetham Eye Institute, Joslin Diabetes Center, Boston, Massachusetts, United States
  • Miguel O. Bernabeu
    Centre for Medical Informatics, Usher Institute, University of Edinburgh, Edinburgh, United Kingdom
  • Charles C Cai
    Beetham Eye Institute, Joslin Diabetes Center, Boston, Massachusetts, United States
  • Jan Lammer
    Department of Ophthalmology and Optometry, Medical University Vienna, Vienna, Austria
  • Lloyd P Aiello
    Beetham Eye Institute, Joslin Diabetes Center, Boston, Massachusetts, United States
    Department of Ophthalmology, Harvard Medical School, Boston, Massachusetts, United States
  • Jennifer K Sun
    Beetham Eye Institute, Joslin Diabetes Center, Boston, Massachusetts, United States
    Department of Ophthalmology, Harvard Medical School, Boston, Massachusetts, United States
  • Footnotes
    Commercial Relationships   Yang Lu, None; Miguel Bernabeu, None; Charles Cai, None; Jan Lammer, None; Lloyd Aiello, None; Jennifer Sun, Boston Micromachine (F), Genentech (F), Optovue (F)
  • Footnotes
    Support  NEI R01 EY024702; JDRF 3-SRA-2014-264-M-R; Beatson Childcare Ambassador Program Foundation; Massachusetts Lions Eye Research Fund
Investigative Ophthalmology & Visual Science September 2016, Vol.57, 5911. doi:
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      Yang Lu, Miguel O. Bernabeu, Charles C Cai, Jan Lammer, Lloyd P Aiello, Jennifer K Sun; Computational Fluid Dynamics Modeling of Diabetic Retinal Microaneurysms (MAs) Based on Adaptive Optics Scanning Laser Ophthalmoscopy (AOSLO). Invest. Ophthalmol. Vis. Sci. 2016;57(12):5911.

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

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Abstract

Purpose : Although the extent and severity of perfused MAs in the diabetic eye have implications for risk of disease progression and visual loss, structural characteristics of individual MAs associated with differences in perfusion are poorly understood. This study utilizes MA morphologic and structural attributes to estimate perfusion characteristics through advanced computational analysis of AOSLO images.

Methods : MAs were captured using AOSLO multiply scattered light imaging with 75-frame videos (30 frames/sec, 2°×2° area). Based on standard deviation maps of MA perfusion, manual segmentation was then performed to generate binary masks. 3-D geometric models were constructed under the assumption that MAs are rotationally symmetric with respect to centerline. MA inlet blood flow was set based on previously described parameters. Computational modeling estimated blood velocity (Vel), perfusion pressure (PP) and wall shear stress (WSS) within each MA.

Results : Six MAs were imaged from 4 eyes of 4 type 1 diabetic subjects with NPDR or PDR (mean±SD age: 44±10 yrs, DM duration: 30±8 yrs, HbA1c: 8.2±0.9, and 3 male). Of MAs studied, 4 were saccular (3 partially clotted) and 2 were fusiform (all fully perfused). Mean±SD Vel, WSS and PP were 70.7±121.2µm/s, 0.18±0.36Pa, and 2.67±0.01mmHg respectively. PP appeared homogeneous throughout each individual MA. Lower Vel and WSS (less than median values) were more commonly observed in saccular vs fusiform MAs (Vel: 78% vs 22%, WSS: 81% vs 19%), with such areas present adjacent to outpouchings of the MA wall furthest away from the inlet and outlet (Figure 1). In 3 partially perfused MAs, intraluminal clots were located adjacent to where Vel was slowest.

Conclusions : This method enables noninvasive, in vivo estimation of perfusion parameters within MAs in the diabetic eye by combining ultrahigh resolution AOSLO imaging with advanced computational modeling. In this small group of lesions, Vel and WSS (but not PP) varied with conformational characteristics of each MA. Further studies will explore whether these parameters may predict MA enlargement, resolution, clotting, or hemorrhage in the diabetic eye.

This is an abstract that was submitted for the 2016 ARVO Annual Meeting, held in Seattle, Wash., May 1-5, 2016.

 

Figure 1. A. AOSLO multiply scattered light image of a saccular MA with partial clot. B. Boundary selection based on the perfusion map. C. 3-D reconstructed model. D. Velocity streamline map. E. Wall shear stress map.

Figure 1. A. AOSLO multiply scattered light image of a saccular MA with partial clot. B. Boundary selection based on the perfusion map. C. 3-D reconstructed model. D. Velocity streamline map. E. Wall shear stress map.

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