All ETDs from UAB

Advisory Committee Chair

Christopher S Coffey

Advisory Committee Members

Gary R Cutter

Charles R Katholi

N Shastry Akella

Donald B Twieg

Document Type


Date of Award


Degree Name by School

Doctor of Philosophy (PhD) School of Public Health


Diffusion tensor imaging (DTI) is a magnetic resonance imaging (MRI) technique capable of in vivo characterization of the spatial and angular dependence of free water diffusion in tissue. By studying intra- and extra-cellular water mobility, inference can be made about the surrounding medium. The non-uniform angular dependence of diffusion, known as anisotropy, is evident in fibrous tissues, which exhibit greater diffusion parallel versus perpendicular to the fiber orientation. DTI’s sensitivity to anisotropy makes it an effective tool for measuring the integrity of fibrous myelinated white matter tracts in the brain. For demyelinating diseases such as multiple sclerosis (MS), DTI has seen extensive use for evaluating disease progression. Several techniques have been developed to analyze the significance of differences between several sets of images. There have been fewer attempts to automate the regression of clinical outcomes using DTI data. I consider the problem of quantifying the connection between observed differences in images and disease related clinical outcomes. I develop a functional linear model approach to automated generation of imaging biomarkers from DTI data. This method is implemented via a two stage model fitting procedure that begins with a wavelet-based voxelwise analysis to extract features related to the outcome. The second stage uses these features to predict the clinical outcome. The method is applied to the analysis of iv longitudinal DTI data from an MS cohort. A comprehensive software suite is developed in R to implement the method.

Included in

Public Health Commons



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