Physics-informed deep generative learning for quantitative assessment of the retina

Brown, Emmeline; Guy, Andrew; Holroyd, Natalie; Sweeney, Paul W.; Gourmet, Lucie; Coleman, Hannah N.; Walsh, Claire; Shipley, Rebecca J.; Rajendram, Ranjan; Walker‐Samuel, Simon

doi:10.1101/2023.07.10.548427

Cited by 2 publications

(1 citation statement)

References 65 publications

(68 reference statements)

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“…The class of space-filling algorithms referred to as constrained constructive optimization (CCO) algorithms is another approach that includes rules and constraints meant to reproduce the angiogenesis process. 25 , 26 It has been applied to the retinal vasculature 27 , 28 but only to create synthetic data for deep-learning applications.…”

mentioning

confidence: 99%

Linking Vascular Structure and Function: Image-Based Virtual Populations of the Retina

Hernandez,

Madhusudhan,

Zheng

et al. 2024

Invest. Ophthalmol. Vis. Sci.

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Purpose This study explored the relationship among microvascular parameters as delineated by optical coherence tomography angiography (OCTA) and retinal perfusion. Here, we introduce a versatile framework to examine the interplay between the retinal vascular structure and function by generating virtual vasculatures from central retinal vessels to macular capillaries. Also, we have developed a hemodynamics model that evaluates the associations between vascular morphology and retinal perfusion. Methods The generation of the vasculature is based on the distribution of four clinical parameters pertaining to the dimension and blood pressure of the central retinal vessels, constructive constrained optimization, and Voronoi diagrams. Arterial and venous trees are generated in the temporal retina and connected through three layers of capillaries at different depths in the macula. The correlations between total retinal blood flow and macular flow fraction and vascular morphology are derived as Spearman rank coefficients, and uncertainty from input parameters is quantified. Results A virtual cohort of 200 healthy vasculatures was generated. Means and standard deviations for retinal blood flow and macular flow fraction were 20 . 80 ± 7 . 86 µL / min and 15 . 04% ± 5 . 42%, respectively. Retinal blood flow was correlated with vessel area density, vessel diameter index, fractal dimension, and vessel caliber index. The macular flow fraction was not correlated with any morphological metrics. Conclusions The proposed framework is able to reproduce vascular networks in the macula that are morphologically and functionally similar to real vasculature. The framework provides quantitative insights into how macular perfusion can be affected by changes in vascular morphology delineated on OCTA.

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mentioning

confidence: 99%

Linking Vascular Structure and Function: Image-Based Virtual Populations of the Retina

Hernandez,

Madhusudhan,

Zheng

et al. 2024

Invest. Ophthalmol. Vis. Sci.

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Linking Structure and Function: Image-Based Virtual Populations of the Retinal Vasculature

Hernandez,

Madhusudhan,

Zheng

et al. 2023

Preprint

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PurposeThis study explores the relationship between microvascular parameters as delineated by optical coherence tomography angiography (OCTA) and retinal perfusion. We introduce a versatile framework to examine the interplay between the retinal vasculature structure and function, generating virtual vasculatures from central retinal vessels to macular capillaries. Alongside this, we develop a haemodynamics model which tests the associations between vascular morphology and retinal perfusion.MethodsThe generation of the vasculature is based on the distribution of four clinical parameters pertaining to the dimension and blood pressure of the central retinal vessels, constructive constrained optimisation and Voronoi diagram. Arterial and venous trees are generated in the temporal retina and connected through three layers of capillaries at different depths in the macula. The correlations between total retinal blood flow and macular flow fraction and vascular morphology are derived as Spearman rank coefficients and uncertainty from input parameters is quantified.ResultsA virtual cohort of 200 healthy vasculatures were generated. Mean and standard deviation for retinal blood flow and macular flow ratio were 19.15±7.34 μL/min and 4.52±1.19 %. Retinal blood flow was correlated with vessel area density, vessel diameter index, fractal dimension and vessel calibre index. The macular flow fraction was not correlated with any morphological metrics.ConclusionsThe proposed framework is able to reproduce vascular networks in the macula that are morphologically and functionally similar to real vasculature. The framework provides quantitative insights into how macular perfusion can be affected by changes in vascular morphology delineated on OCTA.

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Physics-informed deep generative learning for quantitative assessment of the retina

Cited by 2 publications

References 65 publications

Linking Vascular Structure and Function: Image-Based Virtual Populations of the Retina

Linking Vascular Structure and Function: Image-Based Virtual Populations of the Retina

Linking Structure and Function: Image-Based Virtual Populations of the Retinal Vasculature

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