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Please use this identifier to cite or link to this item: http://hdl.handle.net/1807/11141

Title: Analysis of Diffusion MRI Data in the Presence of Noise and Complex Fibre Architectures
Authors: Fobel, Ryan
Advisor: Stanisz, Greg
Department: Medical Biophysics
Keywords: Diffusion tensor imaging
Magnetic Resonance Imaging
Complex fibres
spherical harmonics
generalized tensors
magnitude bias
high b-values
Issue Date: 30-Jul-2008
Abstract: This thesis examines the advantages to nonlinear least-squares (NLS) fitting of diffusion-weighted MRI data over the commonly used linear least-squares (LLS) approach. A modified fitting algorithm is proposed which accounts for the positive bias experienced in magnitude images at low SNR. For b-values in the clinical range (~1000 s/mm2), the increase in precision of FA and fibre orientation estimates is almost negligible, except at very high anisotropy. The optimal b-value for estimating tensor parameters was slightly higher for NLS. The primary advantage to NLS was improved performance at high b-values, for which complex fibre architectures were more easily resolved. This was demonstrated using a model-selection classifier based on higher-order diffusion models. Using a b-value of 3000 s/mm2 and magnitude-corrected NLS fitting, at least 10% of voxels in the brain exhibited diffusion profiles which could not be represented by the tensor model.
URI: http://hdl.handle.net/1807/11141
Appears in Collections:Master
Department of Medical Biophysics - Master theses

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