Advanced reconstruction techniques for high-resolution diffusion-weighted MRI
Abstract/Contents
- Abstract
- Diffusion-weighted MRI (DWI) is a non-invasive and non-ionizing method that detects the tissue microstructure by measuring the Brownian motion of water molecules in our body. It plays an essential role in numerous clinical applications and neuroscience research. However, the spatial resolution and signal-to-noise ratio of conventional DWI are limited due to the use of a single-shot acquisition. Multi-shot imaging enables the acquisition of high-resolution DWI data, but results in ghosting artifacts and signal cancellation. In this dissertation, we present advanced reconstruction techniques to overcome these artifacts and enable high-quality DWI in the clinic. First, we present the usage of the locally low-rank regularization to resolve the motion-induced phase variations among different shots. We demonstrate the improvements of multi-shot imaging with our proposed reconstruction in a clinical breast MRI study. To further improve the image quality, we introduce a non-linear model with a constraint on the magnitude images to utilize the angular correlation between different diffusion-encoding directions. Finally, we accelerate the reconstruction with an unrolled pipeline, which contains recurrences of model-based gradient updates and neural networks alternating between image space and Fourier space. Together, these techniques allow immediate reconstruction of high-resolution high-SNR DWI with reduced scan time requirements
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 | 2020; ©2020 |
| Publication date | 2020; 2020 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Hu, Yuxin |
|---|---|
| Degree supervisor | Hargreaves, Brian Andrew |
| Thesis advisor | Hargreaves, Brian Andrew |
| Thesis advisor | Daniel, Bruce (Bruce Lewis) |
| Thesis advisor | Nishimura, Dwight George |
| Thesis advisor | Pauly, John (John M.) |
| Degree committee member | Daniel, Bruce (Bruce Lewis) |
| Degree committee member | Nishimura, Dwight George |
| 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 | Yuxin Hu |
|---|---|
| Note | Submitted to the Department of Electrical Engineering |
| Thesis | Thesis Ph.D. Stanford University 2020 |
| Location | electronic resource |
Access conditions
- Copyright
- © 2020 by Yuxin Hu
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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