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Title page for ETD etd-03142017-100847


Type of Document Dissertation
Author Harrigan, Robert Louis
Author's Email Address rob.harrigan89@gmail.com
URN etd-03142017-100847
Title Optic Nerve Characterization using Magnetic Resonance Imaging: The Search for Biomarkers
Degree PhD
Department Electrical Engineering
Advisory Committee
Advisor Name Title
Bennett A. Landman Committee Co-Chair
Seth A. Smith Committee Co-Chair
David J. Calkins Committee Member
Jack H. Noble Committee Member
Richard Alan Peters Committee Member
Keywords
  • multi atlas segmentation
  • Optic nerve
  • conjugate gradient descent
  • magnetic resonance imaging
  • quantitative magnetization transfer
Date of Defense 2017-03-07
Availability unrestricted
Abstract
The optic nerve is a vital bundle of axons which carries all visual information from the retina posterior to the brain for higher order processing. The optic nerve and eye orbit are affected by many devastating diseases including optic neuritis, glaucoma and anterior ischemic optic neuritis. This dissertation addresses the use of magnetic resonance imaging for investigating anatomical and microstructural changes in the optic nerve in healthy controls and disease cohorts.

We propose a fully automated pipeline for segmentation of the optic nerve and other eye-orbit structures. This pipeline is applied to large-scale disease cohort to search for correlations between morphological changes and functional visual measures. We introduce a clinically viable advanced MRI sequence for accurate visualization of the optic nerve and sub-arachnoid cerebrospinal fluid. We develop and improve upon an algorithm to automatically estimate optic nerve and surrounding cerebrospinal fluid radius along the length of the optic nerve. We perform a short- and long-term reproducibility study on young healthy controls for algorithm evaluation and publicly release this data for the standardized comparison of future proposed algorithms. We apply this validated automatic radius estimation algorithm to a clinical population of patients with multiple sclerosis to detect differences in patients’ eyes with and without a history of optic neuritis. Finally, we utilize a simulation framework to numerically optimize quantitative magnetization transfer imaging sampling patterns to move towards reducing scan times and increasing clinical viability of quantitative magnetization transfer imaging for microstructural characterization of tissue.

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