Advancing diffusion-weighted magnetic resonance imaging methods for neuronal fiber mapping
Abstract/Contents
- Abstract
- Mapping the complex structural connectivity of the human brain in vivo is essential for understanding healthy brain function and the fundamental basis of many neurological and psychiatric disorders. Diffusion-weighted magnetic resonance imaging (MRI) measures the diffusion pattern of water molecules to infer the underlying tissue microstructure. Coupled with fiber tracking techniques, diffusion-weighted MRI has become a widely utilized non-invasive method for mapping neuronal fiber pathways. Nonetheless, it is challenging to accurately model the diffusion pattern in tissue while a model-free approach requires a lengthy acquisition. Further, the reconstructed fiber model requires rigorous validation for clinical translation. This dissertation addresses these challenges in the course of three projects. First, a thorough analysis of the effects of q-space truncation and sampling on the water molecule displacement ensemble average propagator (EAP) in the model-free q-space imaging (QSI) framework is performed. This study clarifies guidelines for acquiring and reconstructing Cartesian QSI data such that aliasing is prevented in the EAP and and Gibbs ringing is minimized in the estimated fiber orientations. To increase QSI's applicability to different types of data, an intuitive and practical QSI reconstruction framework for obtaining the EAP and fiber orientations from multi-shell q-space samples is proposed. Finally, a retrospective study is conducted to assess the validity and efficacy of diffusion-weighted MRI fiber tracking-based targeting for transcranial MRI-guided focused ultrasound treatment of essential tremor. The studies presented in this dissertation advanced neuronal fiber mapping approaches for diffusion-weighted MRI.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2017 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Tian, Qiyuan |
|---|---|
| Associated with | Stanford University, Department of Electrical Engineering. |
| Primary advisor | McNab, Jennifer (Jennifer A.) |
| Thesis advisor | McNab, Jennifer (Jennifer A.) |
| Thesis advisor | Glover, Gary H |
| Thesis advisor | Pauly, John E. (John Edward), 1927- |
| Advisor | Glover, Gary H |
| Advisor | Pauly, John E. (John Edward), 1927- |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Qiyuan Tian. |
|---|---|
| Note | Submitted to the Department of Electrical Engineering. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2017. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2017 by Qiyuan Tian
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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