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Improved characterization of white matter fiber bundles using diffusion MRI

dc.creatorHong, Xin
dc.description.abstractDiffusion Tensor Imaging (DTI) has become the primary imaging modality for non-invasive characterization of the microstructure of living tissues, particularly of human white matter. Despite its success in various research areas and clinical applications, DTI is unable to describe adequately non-Gaussian diffusion. Fiber ORientation Estimated using Continuous Axially Symmetric Tensors (FORECAST), a new approach to High Angular Resolution Diffusion (HARD) analysis, is able to provide reliable estimates of the fiber radial diffusivity and orientation distribution within each voxel. In this study, several techniques were developed to enhance the FORECAST model’s reproducibility. The model’s dependence on various imaging parameters and analysis parameters was tested by Monte Carlo simulation. The optimal parameters for FORECAST analysis was determined based on the simulation results, and verified by in vivo human data.
dc.subjectCentral nervous system -- Magnetic resonance imaging
dc.subjectanisotropic smoothing
dc.subjectTikhonov regularization
dc.subjecteven-order fitting
dc.subjectDiffusion tensor imaging
dc.titleImproved characterization of white matter fiber bundles using diffusion MRI
dc.contributor.committeeMemberAdam W. Anderson
dc.contributor.committeeMemberMark D. Does
dc.type.materialtext Engineering University

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