Quantitative Analysis of Retinal Vascular Morphology and Dynamics Using Optical Coherence Tomography

As an extension of the central nervous system, the retina offers a unique opportunity for noninvasive examination of the body’s microvasculature and may reveal early signs of systemic and retinal diseases. Optical coherence tomography (OCT) provides depth-resolved, high-resolution imaging, making it suitable for detailed analysis of retinal vasculature. However, challenges remain in accurately characterizing vessel behavior, particularly in artery-vein differentiation and quantifying wall thickness and lumen diameter. This dissertation addresses these challenges through two aims. The first involves development and validation of OCT methods for quantitative vascular analysis. We introduced depth-resolved vascular profile analysis for artery-vein classification, identifying wall boundary patterns and lumen intensity features from B-scans. These features enabled robust vessel type identification in the human retina. We then developed an adaptive depth segmentation method to measure wall thickness and lumen diameter, enabling quantification of the wall-to-lumen ratio (WLR). Comparative analysis in control and 5xFAD mice established WLR as a sensitive marker of Alzheimer’s disease condition. The second aim focuses on dynamic vascular assessment using Doppler OCT. While traditionally used to measure blood flow, we extended its application to capture real-time changes in vessel diameter during the cardiac cycle. This revealed an unreported phenomenon: anisotropic retinal vessel pulsatility, where axial expansion exceeds lateral expansion. Taken together, this work advances OCT-based methods for retinal vascular assessment, contributing new insights for artery-vein classification, vessel morphology quantification, and dynamic analysis relevant to early detection of neurovascular and systemic disease.

Author dc:creator

• Tobiloba Adejumo (14411265)

• Biomedical Engineering

Statement dc:rights

• In Copyright

DOI dc:identifier

https://doi.org/10.25417/uic.30424912.v1

OAI identifier oai:identifier

oai:figshare.com:article/30424912