In Vivo Photoacoustic Imaging of Anterior Ocular Vasculature: A Random Sample Consensus Approach.

Visualizing ocular vasculature is important in clinical ophthalmology because ocular circulation abnormalities are early signs of ocular diseases. Photoacoustic microscopy (PAM) images the ocular vasculature without using exogenous contrast agents, avoiding associated side effects. Moreover, 3D PAM images can be useful in understanding vessel-related eye disease. However, the complex structure of the multi-layered vessels still present challenges in evaluating ocular vasculature. In this study, we demonstrate a new method to evaluate blood circulation in the eye by combining in vivo PAM imaging and an ocular surface estimation method based on a machine learning algorithm: a random sample consensus algorithm. By using the developed estimation method, we were able to visualize the PA ocular vascular image intuitively and demonstrate layer-by-layer analysis of injured ocular vasculature. We believe that our method can provide more accurate evaluations of the eye circulation in ophthalmic applications.