Diffusion Tensor Imaging (DTI) is a well-established magnetic resonance technique
that can non-invasively interpret tissue geometry and track neural pathways by
sampling the diffusion of water molecules in the brain tissue. However, it is currently
limited to tracking large nerve fiber bundles and fails to faithfully resolve thinner
fibers. Conventional DTI studies use a diffusion time, t[subscript diff] of 30 ms - 55 ms for
diffusion measurements. This work proposes the use of DTI at long t[subscript diff] to enhance
the sensitivity of the method towards regions of low diffusion anisotropy and improve
tracking of smaller fibers. The Stimulated Echo Acquisition Mode (STEAM) sequence
was modified to allow DTI measurements at long t[subscript diff] (approximately 200 ms), while
avoiding T2 signal loss. For comparison, DTI data was acquired using STEAM at the
shorter value of t[subscript diff] and with the standard Double Spin Echo sequence with matched
signal-to-noise ratio. This approach was tested on phantoms and fixed monkey brains
and then translated to in vivo studies in rhesus macaques. Qualitative and quantitative
comparison of the techniques was based on fractional anisotropy, diffusivity,
three-phase plots and directional entropy. Tensor-field maps and probabilistic connectivity
fronts were evaluated for all three acquisitions. Comparison of the tracked
nerve pathways showed that fibers obtained at long t[subscript diff] were much longer. Further,
the optic tract was tracked in ex vivo fixed rhesus brains for cross validation. The
optic tract, traced at long t[subscript diff], conformed to the well documented anatomical description,
thus confirming the accuracy of tract tracing at long t[subscript diff]. The benefits of
DTI at long t[subscript diff] indeed help to realize the potential of tensor based tractography
towards studying neural development and diagnosing neuro-pathologies, albeit the
improvement is more significant ex vivo than in vivo.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/29708 |
| Date | 30 June 2009 |
| Creators | Rane, Swati |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Detected Language | English |
| Type | Dissertation |
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