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Activity Number:
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201
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Type:
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Contributed
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Date/Time:
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Monday, July 30, 2007 : 2:00 PM to 3:50 PM
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Sponsor:
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ENAR
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| Abstract - #310084 |
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Title:
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Wavelet-Based Modeling of Clinical Outcomes Using Diffusion Tensor Image Data
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Author(s):
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William Prucka*+ and Christopher S. Coffey and Gary Cutter and Daniel S. Reich
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Companies:
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The University of Alabama at Birmingham and The University of Alabama at Birmingham and The University of Alabama at Birmingham and Johns Hopkins University
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Address:
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2013 Highlands Dr, Hoover, AL, 35244,
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Keywords:
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MRI ; DTI ; diffusion tensor imaging ; wavelets ; functional data analysis ; multiple sclerosis
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Abstract:
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Diffusion tensor imaging (DTI) is an advanced MRI technique capable of in vivo characterization of the spatial and angular dependence of free water diffusion in tissue. Microstructural tissue components act as physical barriers, preferentially inhibiting diffusion along certain directions. This is evident in fibrous tissues, which exhibit greater diffusion parallel versus perpendicular to the fiber orientation. The measured anisotropy reveals fiber tracts, indicating pathways of connectivity and pathological disease processes. We present an exploratory wavelet-based spatial model for predicting outcomes using DTI data. The model is fit on wavelet transformed DTI data and the computed effects are back projected into the spatial domain using the inverse wavelet transformation. The model is tested via simulation and applied to a case/control study in multiple sclerosis.
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