Title page for ETD etd-05182004-132313


Type of Document Master's Thesis
Author Taylor, Alexander James
Author's Email Address altaylo3@vt.edu
URN etd-05182004-132313
Title Diffusion Tensor Imaging: Evaluation of Tractography Algorithm Performance Using Ground Truth Phantoms
Degree Master of Science
Department Electrical and Computer Engineering
Advisory Committee
Advisor Name Title
Dr. Christopher L. Wyatt Committee Chair
Dr. A. Lynn Abbott Committee Member
Dr. Pushkin Kachroo Committee Member
Keywords
  • Diffusion Tensor Imaging
  • Tractography
  • Fast Marching Method
  • Streamline
  • Phantom
  • Magnetic Resonance Imaging
Date of Defense 2004-05-13
Availability unrestricted
Abstract

Diffusion Tensor Magnetic Resonance Imaging (DT-MRI), also known as Diffusion Tensor Imaging (DTI), is a unique medical imaging modality

that provides non-invasive estimates of White Matter (WM) connectivity based on local principal directions

of anisotropic water diffusion.

DTI tractography estimates are a macroscopically sampled description of underlying microscopic structure,

and are therefore of limited validity. The under-sampling of

underlying white matter structure in DTI data gives rise to

Intra-Voxel Orientational Heterogeneity (IVOH), a condition in which

white matter structures of multiple different orientations are averaged

into a single DTI voxel sample, causing a loss of validity in the diffusion

tensor model. Fast Marching Tractography (FMT) algorithms based on fast

marching level set methods have been proposed to better handle the

presence of IVOH in DTI data when compared to older Streamline

Tractography (SLT) methods. However, the actual performance advantage

of any tractography algorithm over another cannot be conclusively stated until

a ground truth standard of comparison is developed.

This work develops an optimized version of the FMT algorithm that is dubbed

the Front Propagation Tractography (FPT) algorithm. The FPT algorithm includes unique

approaches to the speed function, connectivity estimation, and

likelihood estimation components of the FMT framework. The performance of the FPT

algorithm is compared against the SLT algorithm using ground truth software phantom data

and human brain data. Software phantom ground truth experiments compare the performance

of each algorithm in single tract and crossing tract structures for varying levels of diffusion

tensor field perturbation. Human brain estimates in the corpus

callosum yield qualitative comparisons from inspection of 3D visualizations.

A final area of exploration is the construction and analysis of a ground truth physical

DTI phantom manifesting IVOH.

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