Electronic Theses and Dissertations

Identifier

2516

Date

2015

Document Type

Thesis

Degree Name

Master of Science

Major

Electrical and Computer Engr

Concentration

Electrical Engineering

Committee Chair

Eddie Jacobs

Committee Member

Wilburn Reddick

Committee Member

Bashir Morshed

Abstract

Tractography is a non-invasive process for reconstruction, modeling and visualization of neural fibers in the white matter (WM) of human brain. It has emerged as a major breakthrough for neuroscience research due to its usefulness in clinical applications. Two types of tractography approaches: deterministic and probabilistic have been investigated to evaluate their performances on tracking fiber bundles using diffusion tensor imaging (DTI). The images are taken by applying pulsed magnetic fields in multiple gradient directions. After removing the non-brain areas from the images, the diffusion tensor indices for each image voxel are calculated. White matter connectivity of the brain, i.e. tractography, is primarily based upon streamline algorithms where the local tract direction is defined by the principle direction of the diffusion tensor. Simulations are performed using three approaches: fiber assignment by continuous tracking (FACT), probability index of connectivity (PICo) and Gibbs tracking (GT). Simulation results show that probabilistic tractography i.e. PICo and GT can reconstruct longer length of fibers compared to the deterministic approach-FACT but with a cost of high computation time. Moreover, GT handles the more complex fiber configurations of crossing and kissing fibers, more effectively and provides the best reconstruction of fibers. In addition, diffusion tensor indices: fractional anisotropy (FA) and mean diffusivity (MD) for a region of interest can be quantified and used to assess several brain diseases. Prospective investigation of DTI based tractography can reveal useful information on WM architecture in normal and diseased brain which will speed up the detection and treatment of various brain diseases.

Comments

Data is provided by the student.

Library Comment

Dissertation or thesis originally submitted to the local University of Memphis Electronic Theses & dissertation (ETD) Repository.

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