
Proceedings Paper
Structural-functional relationships between eye orbital imaging biomarkers and clinical visual assessmentsFormat | Member Price | Non-Member Price |
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Paper Abstract
Eye diseases and visual impairment affect millions of Americans and induce billions of dollars in annual economic
burdens. Expounding upon existing knowledge of eye diseases could lead to improved treatment and disease prevention.
This research investigated the relationship between structural metrics of the eye orbit and visual function measurements
in a cohort of 470 patients from a retrospective study of ophthalmology records for patients (with thyroid eye disease,
orbital inflammation, optic nerve edema, glaucoma, intrinsic optic nerve disease), clinical imaging, and visual function
assessments. Orbital magnetic resonance imaging (MRI) and computed tomography (CT) images were retrieved and
labeled in 3D using multi-atlas label fusion. Based on the 3D structures, both traditional radiology measures (e.g., Barrett
index, volumetric crowding index, optic nerve length) and novel volumetric metrics were computed. Using stepwise
regression, the associations between structural metrics and visual field scores (visual acuity, functional acuity, visual
field, functional field, and functional vision) were assessed. Across all models, the explained variance was reasonable
(R2 ~ 0.1-0.2) but highly significant (p < 0.001). Instead of analyzing a specific pathology, this study aimed to analyze
data across a variety of pathologies. This approach yielded a general model for the connection between orbital structural
imaging biomarkers and visual function.
Paper Details
Date Published: 24 February 2017
PDF: 7 pages
Proc. SPIE 10133, Medical Imaging 2017: Image Processing, 101331F (24 February 2017); doi: 10.1117/12.2254613
Published in SPIE Proceedings Vol. 10133:
Medical Imaging 2017: Image Processing
Martin A. Styner; Elsa D. Angelini, Editor(s)
PDF: 7 pages
Proc. SPIE 10133, Medical Imaging 2017: Image Processing, 101331F (24 February 2017); doi: 10.1117/12.2254613
Show Author Affiliations
Xiuya Yao, Vanderbilt Univ. (United States)
Shikha Chaganti, Vanderbilt Univ. (United States)
Kunal P. Nabar, Vanderbilt Univ. (United States)
Katrina Nelson, Vanderbilt Univ. (United States)
Shikha Chaganti, Vanderbilt Univ. (United States)
Kunal P. Nabar, Vanderbilt Univ. (United States)
Katrina Nelson, Vanderbilt Univ. (United States)
Andrew Plassard, Vanderbilt Univ. (United States)
Rob L. Harrigan, Vanderbilt Univ. (United States)
Louise A. Mawn, Vanderbilt Univ. School of Medicine (United States)
Bennett A. Landman, Vanderbilt Univ. (United States)
Rob L. Harrigan, Vanderbilt Univ. (United States)
Louise A. Mawn, Vanderbilt Univ. School of Medicine (United States)
Bennett A. Landman, Vanderbilt Univ. (United States)
Published in SPIE Proceedings Vol. 10133:
Medical Imaging 2017: Image Processing
Martin A. Styner; Elsa D. Angelini, Editor(s)
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