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Abstract
Evidence from histopathology and clinical imaging suggest that choroidal anatomy and hemodynamic perfusion are among the earliest changes in retinal diseases such as age-related macular degeneration (AMD). However, how inner choroidal anatomy affects hemodynamic perfusion is not well understood. Therefore, we sought to understand the influences of choroidal microvascular architecture on the spatial distribution of hemodynamic parameters in choriocapillaris from human donor eyes using image-based computational hemodynamic (ICH) simulations. We subjected image-based inner choroid reconstructions from eight human donor eyes to ICH simulation using a kinetic-based volumetric lattice Boltzmann method to compute hemodynamic distributions of velocity, pressure, and endothelial shear stress. Here, we demonstrate that anatomic parameters, including arteriolar and venular arrangements and intercapillary pillar density and distribution exert profound influences on inner choroidal hemodynamic characteristics. Reductions in capillary, arteriolar, and venular density not only reduce the overall blood velocity within choriocapillaris, but also substantially increase its spatial heterogeneity. These first-ever findings improve understanding of how choroidal anatomy affects hemodynamics and may contribute to pathogenesis of retinal diseases such as AMD.
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1 Shenzhen University, State Key Laboratory of Intelligent Construction and Healthy Operation and Maintenance of Deep Underground Engineering, Shenzhen, China (GRID:grid.263488.3) (ISNI:0000 0001 0472 9649); Indiana University-Purdue University, Department of Mechanical and Energy Engineering, Indianapolis, USA (GRID:grid.257413.6) (ISNI:0000 0001 2287 3919)
2 Indiana University-Purdue University, Department of Mechanical and Energy Engineering, Indianapolis, USA (GRID:grid.257413.6) (ISNI:0000 0001 2287 3919); Indiana University School of Medicine, Department of Vascular Surgery, Indianapolis, USA (GRID:grid.257413.6) (ISNI:0000 0001 2287 3919)
3 Indiana University-Purdue University, Department of Mechanical and Energy Engineering, Indianapolis, USA (GRID:grid.257413.6) (ISNI:0000 0001 2287 3919)
4 Indiana University-Purdue University, Department of Mechanical and Energy Engineering, Indianapolis, USA (GRID:grid.257413.6) (ISNI:0000 0001 2287 3919); Imperial College London, Dyson School of Design Engineering, London, UK (GRID:grid.7445.2) (ISNI:0000 0001 2113 8111)
5 University of Virginia School of Medicine, Center for Advanced Vision Science, Charlottesville, USA (GRID:grid.27755.32) (ISNI:0000 0000 9136 933X); University of Virginia School of Medicine, Department of Pathology, Charlottesville, USA (GRID:grid.27755.32) (ISNI:0000 0000 9136 933X)
6 University of Virginia School of Medicine, Center for Advanced Vision Science, Charlottesville, USA (GRID:grid.27755.32) (ISNI:0000 0000 9136 933X); University of Virginia School of Medicine, Department of Pathology, Charlottesville, USA (GRID:grid.27755.32) (ISNI:0000 0000 9136 933X); University of Virginia School of Medicine, Department of Ophthalmology, Charlottesville, USA (GRID:grid.27755.32) (ISNI:0000 0000 9136 933X); University of Virginia School of Medicine, Department of Microbiology, Immunology, and Cancer Biology, Charlottesville, USA (GRID:grid.27755.32) (ISNI:0000 0000 9136 933X)
7 China University of Petroleum (East China), Research Center of Multiphase Flow in Porous Media, Qingdao, China (GRID:grid.497420.c) (ISNI:0000 0004 1798 1132)
8 University of Virginia School of Medicine, Center for Advanced Vision Science, Charlottesville, USA (GRID:grid.27755.32) (ISNI:0000 0000 9136 933X); University of Virginia School of Medicine, Department of Ophthalmology, Charlottesville, USA (GRID:grid.27755.32) (ISNI:0000 0000 9136 933X); University of Virginia School of Medicine, Department of Biomedical Engineering, Charlottesville, USA (GRID:grid.27755.32) (ISNI:0000 0000 9136 933X)