Images of the ONH were acquired by scanning laser tomography with the HRT. The confocal arrangement of the HRT includes a diode laser, beam-splitter, and two optically conjugate pinholes. The beam passes through the center of the pupil and is focused at the retina to a specific point size, its limit set by the properties of the human eye. Reflected light from the beam exits the eye through the pupil, passes a beam splitter and then goes through a confocal pinhole before reaching a solid state detector. This setup is designed to block scattered light and light from other sources from entering the detector. A deflector mirror then moves the laser beam horizontally so an adjacent point can be imaged. After one line has been acquired, a second deflector mirror moves the beam vertically before acquiring another horizontal line. A two-dimensional (2-D) scan is acquired in this rasterlike fashion in approximately 32 ms (a total of 256 × 256 pixels). The plane perpendicular to the optic axis in which the beam is focused, called the focal plane, can be changed and is moved from anterior to posterior, acquiring a total of 32 equally spaced scans. The resultant 3-D (256 × 256 × 32-pixel) image is referred to as a confocal image. The total acquisition time is 1.6 seconds. More details are provided by Zinser.
1
The 3-D confocal image acquired is postprocessed in an attempt to compensate for random eye movements, such as microsaccades and slow drift, known to occur when a subject is asked to fixate on a target.
23 A topography image is then formed by calculating the position of maximum reflectivity at each
z-profile (a one-dimensional signal of 32 intensity values parallel to the optical axis). The intensity at each pixel in the resultant topography image represents the surface height of the ONH or surrounding papillary retina. Typically, three topography images are acquired at each visit. The topographies are normally merged to calculate a mean topography. Image registration algorithms within the proprietary HRT software align the topography images for interscan differences in examination positions.
The repeatability of the images obtained was quantified by the mean pixel height standard deviation (MPHSD). This metric is effectively a gauge of the variability of each pixel height measurement across the three topographies used to make up the mean topography and is used in this study to report intrascan repeatability.
24 It is calculated from the standard deviations at each pixel across the mean topographic image (i.e., the MPHSD is the mean of 256 × 256 pixel height standard deviations). Previous studies have used MPHSD to evaluate the repeatability of the technology in normal subjects and patients with glaucoma
25 26 It has been shown that MPHSD is influenced by lens opacity, age, and degree of astigmatism.
11 In this study, MPHSD was also used to report the repeatability of mean topographies, referred throughout as interscan repeatability.
Confocal scanning laser tomography has known limitations. For example, although the optical setup is designed to reject most light from outside the focal plane, it by no means rejects all it and an out-of-focus haze remains.
27 28 The resolution in the confocal images is higher in the
x and
y directions compared with poorer resolution along the optical axis (
z-axis). The resolution obtained is also limited by the optics of the eye, aberrations are generated by the cornea and the lens.
29 This results in axial smearing: For example, if a spherical point object is being imaged with constant reflectivity properties, the resultant image obtained will appear elongated in the
z-axis. Another limitation of the technology is that the detector in the optical setup, is prone to Poisson noise, primarily due to quantum variations in the number of photons recorded.
23 This noise obscures real data and randomly creates impossible features such as high-intensity data only one pixel in size.
22 A further discussion on the principals and limitation of imaging systems is given by Goodman.
30
Deconvolution is an example of an image restoration algorithm that models the imaging system with:
\[g(r){=}{\int}h(r{\vert}r{^\prime})f(r{^\prime})dr{^\prime}{+}n(r)\]
where
g(
r) is the image obtained in direction
r,
f(
r) is the true image,
n(
r) is noise,
h(
r|r′) is the point-spread function (PSF): the image brightness at location
r of a point source located at position
r′. The PSF describes how much a single-point source of light is spread through the focal planes. The image formed by a system
g(
r) is a convolution of the PSF (across the whole geometrical image area) with the brightness at each point source. The wider the PSF the more blur the image will contain. In confocal scanning laser tomography, the PSF is assumed to have a three dimensional hour-glass shape, orientated along the optical axis and of highest intensity in the central “narrow” area.
22 The objective of image restoration algorithms is to obtain an estimate of the true image
f(
r), given the image obtained
g(
r). In classic linear deconvolution, the PSF
h(
r|r′) is assumed to be known explicitly before the procedure. A long list of these techniques is available, such as the inverse filter and Wiener filter.
31 32 Unfortunately, in our situation, the blur
h(
r|r′) is unknown, along with much information about the true image
f(
r). Blind deconvolution refers to the task of separating two convolved signals
f(
r) and
h(
r|r′), when both signals are either unknown or partially known.