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. 2015:2015:293693.
doi: 10.1155/2015/293693. Epub 2015 Oct 18.

Optical Coherence Tomography as a Tool for Ocular Dynamics Estimation

Damian Siedlecki  1 , Waldemar Kowalik  1 , Henryk Kasprzak  1
Affiliations

Optical Coherence Tomography as a Tool for Ocular Dynamics Estimation

Damian Siedlecki et al. Biomed Res Int. 2015.

Abstract

Purpose: The aim of the study is to demonstrate that the ocular dynamics of the anterior chamber of the eye can be estimated quantitatively by means of optical coherence tomography (OCT).

Methods: A commercial high speed, high resolution optical coherence tomographer was used. The sequences of tomographic images of the iridocorneal angle of three subjects were captured and each image from the sequence was processed in MATLAB environment in order to detect and identify the contours of the cornea and iris. The data on pulsatile displacements of the cornea and iris and the changes of the depth of the gap between them were retrieved from the sequences. Finally, the spectral analysis of the changes of these parameters was performed.

Results: The results of the temporal and spectral analysis manifest the ocular microfluctuation that might be associated with breathing (manifested by 0.25 Hz peak in the power spectra), heart rate (1-1.5 Hz peak), and ocular hemodynamics (3.75-4.5 Hz peak).

Conclusions: This paper shows that the optical coherence tomography can be used as a tool for noninvasive estimation of the ocular dynamics of the anterior segment of the eye, but its usability in diagnostics of the ocular hemodynamics needs further investigations.

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Figures

Figure 1
Figure 1
The stages of the image processing for the reconstruction of the posterior surface of the cornea and the anterior surface of the iris. The effect of the “bended” cornea on the original image is an artefact that can be associated with the Fourier transform of the optical signal reaching the light detector and the relatively short axial range of the instrument (c.a. 2.5 mm). The image of superposition of the identified surfaces and data after edge detection is only for demonstration purposes.
Figure 2
Figure 2
An example of temporal fluctuation of the (a) posterior cornea, (b) anterior iris, and (c) depth D of the gap between the cornea and the iris being a result of the data processing of the sequence captured for Subject #1. Both plots in the row represent the same data, but the pulsations are better demonstrated in the contour plots.
Figure 3
Figure 3
Fourier spectra of displacement data obtained from the sequence captured for Subject #1: (a) the results for the posterior surface of the cornea; (b) the results for the anterior surface of the iris; (c) the results for the distance D between the cornea and iris; (d) the power spectra averaged over the x coordinate. The spectral analysis was performed as the one-dimensional fast Fourier transform of the temporal changes of the surfaces.
Figure 4
Figure 4
Fourier spectra of displacement data obtained from the sequence captured for Subject #2: (a) the results for the posterior surface of the cornea; (b) the results for the anterior surface of the iris; (c) the results for the distance D between the cornea and iris; (d) the power spectra averaged over the x coordinate. The spectral analysis was performed as the one-dimensional fast Fourier transform of the temporal changes of the surfaces.
Figure 5
Figure 5
Fourier spectra of displacement data obtained from the sequence captured for Subject #3: (a) the results for the posterior surface of the cornea; (b) the results for the anterior surface of the iris; (c) the results for the distance D between the cornea and iris; (d) the power spectra averaged over the x coordinate. The spectral analysis was performed as the one-dimensional fast Fourier transform of the temporal changes of the surface.
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