Glaucoma is the second leading cause of blindness in the world. The disease is characterized by irreversible damage to retinal ganglion cells. Once glaucoma is
detected, further vision loss can be prevented by pharmacological or surgical treatment. However, current diagnostic methods lack the necessary sensitivity and up to 40% of vision maybe irreversibly lost before detection occurs.
A Swept Source Polarization-Sensitive Optical Coherence Tomography (SS-PSOCT) instrument for high sensitivity cross-sectional imaging of optical anisotropy in turbid media has been designed, constructed, and verified. A multiple-state nonlinear fitting algorithm was used to measure birefringence of the retinal nerve fiber layer with
less than 1%± average uncertainty.
To perform eye imaging efficiently a slit-lamp based interface for the SS-PSOCT instrument with a Line Scanning Laser Ophthalmoscope (LSLO) was used. This interface allowed for repeatable, stable, and registered measurements of the retina. A fixation target was used to stabilize the volunteer’s eye and image desired areas of the
retina. The LSLO allowed for an optimization of the location of OCT scans on the retina and provided a fundus blood vessel signature for registration between different imaging sessions.
The SS-PSOCT system was used to measure depth-resolved thickness,
birefringence, phase retardation and optic axis orientation of the retinal nerve fiber layer in normal volunteers. The peripapillary area around the optic nerve head (ONH) is most sensitive to glaucoma changes and hence data was acquired as concentric ring scans about the ONH with increasing diameters from 2mm to 5mm. Imaging of normal
patients showed that higher values of phase retardation occurred superior and inferior to the optic nerve head especially next to blood vessels and thicker parts of the retinal nerve fiber layer. / text
| Identifer | oai:union.ndltd.org:UTEXAS/oai:repositories.lib.utexas.edu:2152/ETD-UT-2010-05-1361 |
| Date | 20 October 2010 |
| Creators | Elmaanaoui, Badr |
| Source Sets | University of Texas |
| Language | English |
| Detected Language | English |
| Type | thesis |
| Format | application/pdf |
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