<p>Diffusion tensor imaging (DTI) has emerged as a unique method to characterize brain tissue microstructure non-invasively. DTI typically provides the ability to study white matter structure with a standard voxel resolution of 8μL over imaging field-of-views of the extent of the human brain. As such, it has long been recognized as a promising tool not only in clinical research for the diagnostic and monitoring of white matter diseases, but also for investigating the fundamental biological principles underlying the organization of long and short-range cortical networks. However, the complexity of brain structure within an MRI voxel makes it difficult to dissociate the tissue origins of the measured anisotropy. The tensor characterization is a composite result of proton pools in different tissue and cell structures with diverse diffusion properties. As such, partial volume effects introduce a strong bias which can lead to spurious measurements, especially in regions with a complex tissue structure such as interdigitating crossing fibers or in convoluted cortical folds near the grey/white matter interface.</p><p>This dissertation focuses on the design and development of acquisition and image reconstruction strategies to improve the spatial resolution of diffusion imaging. After a brief review of the theory of diffusion MRI and of the basic principles of streamline tractography in the human brain, the main challenges to increasing the spatial resolution are discussed. A comprehensive characterization of artifacts due to motion and field inhomogeneities is provided and novel corrective methods are proposed to enable the acquisition of diffusion weighted data with 2D mulitslice imaging techniques with full brain coverage, increased SNR and high spatial resolutions of 1.25×1.25×1.25 mm<super>3</super> within an acceptable scan time. The method is extended to a multishot k<sub>_z</sub>-encoded 3D multislab spiral DTI and evaluated in normal human volunteers.</p><p>To demonstrate the increased SNR and enhanced resolution capability of the proposed methods and more generally to assess the value of high-spatial resolution in diffusion imaging, a study of cortical depth-dependence of fractional anisotropy was performed at an unprecedented <italic>in-vivo</italic> inplane resolution of 0.390×0.390μm<super>2</super> and an investigation of the trade-offs between spatial resolution and cortical specificity was conducted within the connectome framework.</p> / Dissertation
| Identifer | oai:union.ndltd.org:DUKE/oai:dukespace.lib.duke.edu:10161/7126 |
| Date | January 2013 |
| Creators | Guidon, Arnaud |
| Contributors | Song, Allen W |
| Source Sets | Duke University |
| Detected Language | English |
| Type | Dissertation |
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