Diffusion encoding waveform design for mapping microstructure in the human brain
Abstract/Contents
- Abstract
- Diffusion MRI is a non-invasive method of measuring the diffusion of water molecules in biological tissue. The patterns of water diffusion reflect the underlying tissue microstructure, which hinders or restricts the displacement of water. Changes in diffusion reflect microstructural changes that can be critical for clinical diagnosis, disease monitoring, and the study of neurodegenerative processes. The diffusive motion of water is encoded through the interaction of time-varying magnetic field gradients and the diffusive motion of water molecules. The design of these gradient waveforms can be tailored to the diffusion regime of interest and the capabilities of the MR scanner. Diffusion gradient waveforms can be designed to separate the effects of tissue heterogeneity, orientation dispersion, and microscopic diffusion anisotropy within the constraints of the limitations and imperfections of the gradient systems. In this dissertation, a parsimonious sampling scheme for double diffusion encoding (DDE) MRI is presented, which optimizes the measurement of microscopic diffusion anisotropy on a whole- body clinical MRI scanner. The proposed scheme was used to demonstrate the improved ability of DDE compared to conventional MRI to characterize normal and damaged tissue in a cohort of multiple sclerosis patients. Next, an optimization framework is presented for the design of generalized diffusion encoding waveforms which are robust to imperfections in the gradient system caused by eddy current and concomitant fields. Finally, the adaptation of these methods to a novel high- performance head-only gradient system is described.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2019; ©2019 |
| Publication date | 2019; 2019 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Yang, Grant Kaijuin | |
|---|---|---|
| Degree supervisor | Hargreaves, Brian Andrew | |
| Degree supervisor | McNab, Jennifer (Jennifer A.) | |
| Thesis advisor | Hargreaves, Brian Andrew | |
| Thesis advisor | McNab, Jennifer (Jennifer A.) | |
| Thesis advisor | Pauly, John (John M.) | |
| Degree committee member | Pauly, John (John M.) | |
| Associated with | Stanford University, Department of Electrical Engineering. |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Grant Kaijuin Yang. |
|---|---|
| Note | Submitted to the Department of Electrical Engineering. |
| Thesis | Thesis Ph.D. Stanford University 2019. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2019 by Grant Kaijuin Yang
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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