Currently, the clinical assessment of optic nerve swelling is limited by the subjective ophthalmoscopic evaluation by experts in order to diagnose and differentiate the cause of the optic disc edema. The long-term goal of our research effort is to develop automated 3D image-analysis approaches for the identification of an optimal set of 3D parameters to quantify the severity of optic nerve edema over time and to help differentiate the underlying cause. The overall objective in this application is to develop strategies, using spectral-domain optical coherence tomography (SD-OCT), to rapidly and accurately determine the severity of optic nerve swelling in patients diagnosed with papilledema and to ascertain morphological features that differentiate papilledema from other disorders causing optic nerve edema. The central hypothesis is that information about volumetric and shape parameters obtainable from 3D image analysis techniques will improve the ability to accurately assess the severity and cause of optic disc edema over the existing subjective ophthalmoscopic assessment of optic nerve swelling using the Fris?n scale or current 2D OCT parameters. The rationale for the proposed research is that having such 3D parameters will dramatically improve the way optic disc swelling is assessed. The following specific aims will be pursued: 1. Develop and evaluate the methodology for computing novel volumetric and shape parameters of a swollen optic nerve head from SD-OCT. This will be completed by refining and evaluating our novel 3D graph-based segmentation algorithms in SD-OCT volumes of patients with optic disc swelling. 2. Identify SD-OCT parameters that optimally correlate with clinical measurements of severity in patients with papilledema and develop a continuous severity scale. This will be accomplished by using machine-learning approaches to relate SD-OCT parameters to expert-defined Fris?n scale grades (a fundus-based measure of severity). It is anticipated that volumetric 3D parameters will more closely correlate with clinical measures than 2D parameters and will provide a continuous severity scale. 3. Identify SD-OCT parameters that differentiate papilledema from other causes of optic disc swelling (or apparent optic disc swelling, as in pseudopapilledema) and develop a corresponding predictive classifier. Our working hypothesis is that 3D shape parameters, especially those near Bruch's membrane opening, will contribute the most in the automatic differentiation process. The approach is innovative because the 3D image-analysis methodology developed by the applicants enables novel determination of 3D volumetric and shape parameters and represents a significant improvement over the status quo of using qualitative image information and 2D OCT image information for assessing optic disc swelling. The proposed research is significant because it will help to establish a much-needed alternative and more objective method by which to assess the severity and cause of optic disc swelling.

Public Health Relevance

The proposed research is relevant to public health because the identification of novel spectral-domain optical coherence tomography parameters for assessing the severity and cause of optic nerve edema is expected to enable more efficient and effective diagnosis and management strategies of patients with such swelling. Thus, the proposed research is relevant to the part of NIH's mission that pertains to developing fundamental knowledge to reduce the burdens of disability and is particularly relevant to the part of NEI's mission regarding understanding blinding eye diseases and preserving sight.

Agency
National Institute of Health (NIH)
Institute
National Eye Institute (NEI)
Type
Research Project (R01)
Project #
1R01EY023279-01
Application #
8477880
Study Section
Special Emphasis Panel (NOIT)
Program Officer
Chin, Hemin R
Project Start
2013-05-01
Project End
2018-04-30
Budget Start
2013-05-01
Budget End
2014-04-30
Support Year
1
Fiscal Year
2013
Total Cost
$339,750
Indirect Cost
$114,750
Name
University of Iowa
Department
Engineering (All Types)
Type
Schools of Engineering
DUNS #
062761671
City
Iowa City
State
IA
Country
United States
Zip Code
52242
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Garvin, Mona K; Lee, Kyungmoo; Burns, Trudy L et al. (2013) Reproducibility of SD-OCT-based ganglion cell-layer thickness in glaucoma using two different segmentation algorithms. Invest Ophthalmol Vis Sci 54:6998-7004