Quantitative measurements and artifact correction methods in body magnetic resonance imaging
Abstract/Contents
- Abstract
- Magnetic Resonance Imaging (MRI), with its flexibility and good soft tissue contrast, is a powerful modality for imaging tissues such as organs, muscle, and cartilage. MRI of cartilage can provide not only morphological images but also quantitative estimates of the tissue, aiding in early detection of diseases such as osteoarthritis, which affects tens of millions of US adults at tremendous societal cost. One of the most promising techniques for this purpose is the Double-Echo in Steady-State (DESS) sequence, a steady-state sequence providing two distortion-free images with high SNR efficiency. However, DESS has complicated signal contrast, can suffer from motion artifacts, and can be sensitive to noise for some quantitative estimates. Another challenging problem in body MRI is imaging close to metallic implants, which distort the magnetic field. Sequences have been developed to overcome this, but they tend to have long scan times. In this thesis, methods are introduced to improve anatomical and quantitative imaging using DESS. This will involve exploring novel signal models for DESS which allow for improved quantification and artifact reduction. Improvements in image quality and quantification are demonstrated. Reduced noise sensitivity of diffusion estimates from DESS by the choice of scan parameters and post processing method is shown as well. A separate method is also presented that greatly accelerates MRI close to metal implants by data undersampling. The resulting aliasing does not corrupt the image due to the pattern of the signal distortion caused by the metal.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2016 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Sveinsson, Bragi |
|---|---|
| Associated with | Stanford University, Department of Electrical Engineering. |
| Primary advisor | Hargreaves, Brian Andrew |
| Thesis advisor | Hargreaves, Brian Andrew |
| Thesis advisor | Gold, Garry E |
| Thesis advisor | Nishimura, Dwight George |
| Advisor | Gold, Garry E |
| Advisor | Nishimura, Dwight George |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Bragi Sveinsson. |
|---|---|
| Note | Submitted to the Department of Electrical Engineering. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2016. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2016 by Bragi Sveinsson
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
Also listed in
Loading usage metrics...