Model-based motion compensation for corneal topography by optical coherence tomography

Abstract

Corneal topography is an essential tool in ophthalmology, in particular for surgical planning and diagnostics. Optical coherence tomography (OCT) enables cross-sectional or volumetric imaging with high resolution. It is, however, not widely used for corneal topography. A major reason for this is that conventional beam-scanning OCT is susceptible to eye motion compared to established modalities, which measure corneal shape in a single shot. To overcome this limitation, we propose a novel pipeline for motion-compensated OCT-based corneal topography. The pipeline includes three main features: (1) continuous, two-dimensional scanning; (2) the three-dimensional continuous motion compensation in postprocessing; and (3) regularised Zernike reconstruction. First, we evaluated our method on an eye phantom that is moved to mimic typical eye motion. The proposed motion compensation was able to determine and correct the movements of the phantom. Second, we performed an in vivo study on 48 eyes, measuring each eye twice with our OCT-based topography, Placido disc topography (Atlas 9000, Carl Zeiss Meditec), and Scheimpflug (Pentacam, Oculus) topography. We then compared the performance of the OCT-based topography to the reference topographies in terms of repeatability and equivalence. The results confirm the necessity and efficiency of the presented motion compensation and validate the proposed methods for scanning and reconstruction.

Higher-order regression three-dimensional motion-compensation method for real-time optical coherence tomography volumetric imaging of the cornea

“…While a wealth of methods has been developed and explored to compensate for ocular motion during OCT imaging of the retina (both hardware-based 11 , 12 and software-based solutions 13 – 18 ), fewer works have focused on motion-compensated OCT-based measurements of the cornea. 5 , 19 , 20 Most of those are targeted toward corneal topography applications, 19 , 20 assume the surface to be perfectly spherical 20 or use a spherical model to estimate corneal curvature, 5 and are based on time-costly postprocessing algorithms. 5 , 18 , 19 …”

“… 5 , 19 , 20 Most of those are targeted toward corneal topography applications, 19 , 20 assume the surface to be perfectly spherical 20 or use a spherical model to estimate corneal curvature, 5 and are based on time-costly postprocessing algorithms. 5 , 18 , 19 …”