No description available.

Despite being considered the current reference standard for perimetric testing in glaucoma, standard automated perimetry has several cardinal limitations, including an unacceptably high test-retest variability, which increases with increasing depth of defect, and a limited useable dynamic range, with test-retest variability spanning almost the entire instrument range in advanced glaucomatous damage. Prior studies have shown that spatial summation, the mechanism by which the visual system integrates light energy across the area of a stimulus, differs in disease, with an enlarged Ricco’s area (the limit of complete spatial summation) found in individuals with glaucoma. The aim of this work was to investigate whether a perimetric stimulus designed to exploit these changes in spatial summation would enable a greater signal/noise ratio (SNR) than that of the current standard stimulus, by directly measuring the displacement of the spatial summation function in glaucoma. Three stimulus forms were developed; one varying in area alone, one varying in both area and contrast simultaneously, and one varying in contrast alone, all operating within the local Ricco’s area. These novel stimuli were compared with the standard Goldmann III stimulus, in terms of disease signal, noise, and SNR. The experiments presented in this thesis indicate that a stimulus modulating in area alone may offer greater benefits for measuring glaucomatous changes in spatial summation in a clinical setting, in the form of a greater disease signal, more uniform response variability with depth of defect, and greater SNR, when compared with the standard Goldmann III stimulus. Additionally, there is some indication that this stimulus is more robust to the effects of intraocular straylight than the Goldmann III stimulus, although test-retest variability and robustness to optical defocus are largely similar. As this work represents the early investigations of this stimulus, further work is required to examine its translation into a clinical environment.

The research described in this thesis presents the development and evaluation of a prototype multispectral, imaging densitometer. Ultimately, the aim was to develop a method of simultaneously isolating the contributions from the three main photoreceptors, across the retina. Thus, providing the clinician with a viable tool for assessing outer retinal function both efficiently and directly. The construction and use of the prototype multispectral imaging densitometer was described in detail. Healthy participants were recruited and imaged to evaluate the baseline capability of the device. The results were validated through comparison to published values for optical density, regeneration rates and photopigment distribution as obtained through in vivo, in vitro and in situ techniques. A novel ocular reflection model was developed to characterise and mathematically remove the pre-receptoral stray light from the images. Whilst the preliminary investigation returned regeneration rates in line with values from the literature, a ‘red shift’ of approximately 30nm was highlighted in the experimental absorption spectra of the visual pigments, hindering their isolation. Investigation into this phenomenon suggested that the cause was pre-receptoral stray light, with both the crystalline lens and the inner retinal layers contributing to the effect. Attempts to remove the crystalline lens component showed a partial reduction of the ‘red shift’. Greater success was achieved through the modelling of the experimental ocular reflection data to simultaneously characterise the contribution from both components. This research has shown that mapping of visual pigment optical density across the retina is possible with the retinal densitometer described. With further development, the technique is likely to become a useful tool in the diagnosis and monitoring of outer retinal disease.

digitalisering@umu.se

No description available.

Glaucoma is a chronic progressive disease that causes loss of retinal ganglion cells, which impairs the visual field. In optic coherence tomography (OCT) image, the retinal ganglion cell (RGC) axons in the optic nerve head (ONH) can be quantified as the minimal thickness from the ONH Pigmental epithelium Central Limit (OPCL) to the Inner limit of the Retina Closest Point (IRCP). Alternatively, the minimal cross-sectional surface area can be measured. In peripapillary atrophy, the morphometry of the retinal pigmental epithelium is affected. Purpose: To design and test a new computational algorithm for estimation of Pigment epithelium to Inner limit of the Retina Minimal Area (PIMA) and evaluate a new method to estimate the Pigment epithelium to Inner limit of the Retina Minimal Distance (PIMD). OPCL can be detected and annotated by a deep learning algorithm in individuals with peripapillary atrophy. Methods: A deep learning algorithm has been trained to automatically detect OPCL, IRCP and calculate PIMD. A new computational algorithm was developed to estimate PIMA in OCT images of young adults. The mean between the first and second version of estimating PIMD was evaluated. The difference of distance between the ONH center-OPCL and ONH center-atrophic edge was estimated in eyes with peripapillary atrophy. Results: A 95% confidence interval for PIMA-2π was estimated to 1.97 ± 0.19 mm2 (df = 15). A confidence interval for the difference between PIMDv1-2π and PIMDv2-2π was 0 ± 1 μm (df = 15). A 95 % confidence interval for the mean difference between ONH-OPCL and ONH-atrophic edge was estimated to 692 ± 192 µm (df = 5). Conclusions: The computational algorithm for estimation of PIMA was developed and applied. An initial analysis indicated the capacity of the deep learning algorithm to detect OPCL in subjects with PPA. Keywords: deep learning, optic nerve head, ONH, retinal pigmental epithelium, RPE, PIMD, PIMD-2π, minimal distance, PIMA, PIMA-2π, minimal area, peripapillary atrophy, PPA, optic coherence tomography, OCT, glaucoma, quantification, retinal ganglion cell axons

No description available.

No description available.

No description available.

No description available.