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Diffusion Tensor Imaging: Evaluation of Tractography Algorithm Performance Using Ground Truth Phantoms

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dc.contributor.authorTaylor, Alexander Jamesen
dc.contributor.committeechairWyatt, Christopher L.en
dc.contributor.committeememberAbbott, A. Lynnen
dc.contributor.committeememberKachroo, Pushkinen
dc.contributor.departmentElectrical and Computer Engineeringen
dc.date.accessioned2014-03-14T20:37:28Zen
dc.date.adate2004-05-21en
dc.date.available2014-03-14T20:37:28Zen
dc.date.issued2004-05-13en
dc.date.rdate2004-05-21en
dc.date.sdate2004-05-18en
dc.description.abstractDiffusion 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.en
dc.description.degreeMaster of Scienceen
dc.identifier.otheretd-05182004-132313en
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-05182004-132313/en
dc.identifier.urihttp://hdl.handle.net/10919/32946en
dc.publisherVirginia Techen
dc.relation.haspartalextaylor_thesis.pdfen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectDiffusion Tensor Imagingen
dc.subjectTractographyen
dc.subjectFast Marching Methoden
dc.subjectStreamlineen
dc.subjectPhantomen
dc.subjectMagnetic Resonance Imagingen
dc.titleDiffusion Tensor Imaging: Evaluation of Tractography Algorithm Performance Using Ground Truth Phantomsen
dc.typeThesisen
thesis.degree.disciplineElectrical and Computer Engineeringen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameMaster of Scienceen

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