Image-Based Biomarkers in Retinal Disease: Applications in Macular Degeneration


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The purpose of this research was to investigate and objectively quantify image-based biomarkers of ocular injury and disease using a combination of non-invasive high resolution imaging techniques. High resolution imaging and sensing provides the basis for the diagnosis of most retinal diseases so effective characterization of endogenous molecular and morphological biomarkers may provide greater insight into the underlying biological processes responsible for tissue damage and visual dysfunction. Moreover, the non-invasive nature of the data acquisition provides an opportunity to investigate dynamic effects and establish a more complete spatial and temporal profile of disease processes within the retina. The intrinsic optical properties of the eye allow us to quantify differences in depth resolved scattering and fluorescence signatures directly related to retinal pathology. The research presented in this dissertation consists of multiple studies aimed at utilizing high resolution imaging tools to identify and objectively analyze image-based biomarkers of retinal degeneration. A combination of imaging and enhancement techniques thatincluded confocal scanning laser ophthalmoscopy, white light fundoscopy, optical coherence tomography and adaptive optics were used for the in vivo data collection. To achieve these goals we investigated small animal models of retinal disease, small animal models of inducible retinal injury and human subjects exhibiting retinal pathology. Collectively, these studies demonstrate the utility of multimodal retinal imaging in the detection and monitoring of perturbations within the retina discrete layers of the retina. The complementary visualization techniques provided multiple metrics which may be used validate the utility of small animal models and establish new clinical criteria for the assessment of ocular diseases at a cellular or even molecular level.



Retina, Macular Degeneration, AMD, Adaptive Optics, SLO, OCT