Evaluating the repeatability of corneal elevation through calculating the misalignment between Successive topography measurements during the follow up of LASIK

Abstract

The study aims to evaluate, using the Iterative Closest Point (ICP) algorithm, the repeatability of successive corneal elevation measurements taken post-LASIK. Two topography maps of 98 LASIK participants were recorded preoperatively (Pre), 1 month (Pos1M) and 3 months postoperatively (Pos3M). Elevation of the second measurement was fitted to the first measurement by calculating using ICP, and correcting for, both translational and rotational misalignment components. The RMS of elevation differences between anterior corneal measurements were statistically significant post-LASIK compared to preoperation (P < 0.05). A misalignment ratio used to describe the weighting of the elevation difference caused by misalignment relative to the total difference remained stable (0.40 and 0.23 for anterior and posterior corneal surfaces, respectively) in different periods. The study also considered the combined misalignment parameter (CM), which represents the total effect of all individual misalignment components on the repeatability of corneal topography maps. CM was significantly greater post-LASIK relative to pre-LASIK (P < 0.05). Overall, the contribution of misalignment to the total difference between successive corneal measurements remained stable pre and post operation, while the combined effect of refractive error correction and optical diameter appeared to have a significant influence on the elevation repeatability in the early stages of the follow up period.

Repeatability of corneal elevation maps in keratoconus patients using the tomography matching method

“…A possible complication is that most tomography methods provide elevation data at a set of regularly-spaced discrete points, and therefore misalignment between successive measurements (either taken in the same setting to check repeatability or separated by a time period to check progression) can mean a different set of points is measured every time, leading to considerable differences in results. This study attempts to assess the effectiveness of a surface matching technology – an Iterative Closest Point (ICP) algorithm, developed in an earlier study 10 , 11 . As a feature-based surface matching technique and the dominant method for image registration, ICP checks the similarities between overlapping maps to determine the rigid-body transformations needed for the best possible match.…”