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Towards Real-time Estimation of a Spherical Eye Model based on a Single Fiber OCT

Boekbijdrage - Boekhoofdstuk Conferentiebijdrage

Depth perception remains a key challenge in vitreoretinal surgery. Currently users only have the view from an overhead stereo-microscope available, but this means of visualisation is quite restrictive. Optical Coherence Tomography (OCT) has been introduced in the past and is even integrated in a number of commercial systems to provide a more detailed depth vision, even showing subsurface structures up to a few millimeters below the surface. The intra-operative use of OCT, especially in combination with robotics, is still sub-optimal. At present one can get either a very slowly updating large volume scan (C-scan) or a faster but not aligned cross-sectional B-scan or an even more local single point A-scan at very high update rate. In this work we propose a model-mediated approach. As the posterior eye segment can be approximated as a sphere, we propose to model the retina with this simplified sphere model, the center and radius of which can be estimated in real time. A time-varying Kalman filter is proposed here in combination with an instrument-integrated optical fiber that provides high-frequency A-scans along the longitudinal instrument direction. The model and convergence of the model has been validated first in a simulation environment and subsequently using an OCT A-scan probe mounted on a co-manipulated vitreoretinal robotic system. The probe was manipulated to measure a 3D stereo lithographically printed spherical model of the posterior eye segment. The feasibility of the proposed method was demonstrated in various scenarios. For the validation a 20 micrometer error and convergence speed of 2.0 seconds was found when sampling A-scans at 200Hz.
Boek: Proceedings 2019 IEEE International Conference on Advanced Robotics
Pagina's: 666 - 672
ISBN:978-1-7281-2467-4
Jaar van publicatie:2019
BOF-keylabel:ja
IOF-keylabel:ja
Authors from:Government, Higher Education
Toegankelijkheid:Closed