Methods for robust diffusion weighted body magnetic resonance imaging
Abstract/Contents
- Abstract
- Diffusion weighted (DW) imaging is an increasingly common Magnetic Resonance Imaging (MRI) contrast in the clinic. There are several clinical applications that do not yet benefit from diffusion weighted imaging, owing to image artifacts encountered by conventional diffusion acquisitions. DW Fast Spin Echo (FSE) sequences are robust to field non-uniformities, but the non Carr-Purcell-Meiboom-Gill (CPMG) artifact must be addressed. Two different novel imaging reconstructions that can be applied to address the non CPMG artifact, were developed. The first reconstruction leverages calibrationless imaging and compressed sensing ideals to reduce the effect of T2 blur using shot locally low rank regularization. Combined with a diffusion prepared stimulated echo FSE sequence, it is shown to have reduced image distortion compared to conventional multi-shot DW-EPI techniques, and improved apparent resolution compared to vendor provided distortionless DW-FSE PROPELLER. The second reconstruction is a joint linear reconstruction for multi-shot quadratic phase increment data that can be acquired without a signal penalty. This reconstruction corrects ghosting artifacts from both shot-to-shot phase and intra-shot signal oscillations. A thorough analysis on the condition number of the proposed linear system shows that the conditioning is the same as applying parallel imaging to each shot, in the worst case, and the same as a standard multi-shot reconstruction in the best case. A clinically feasible sequence that achieves DW imaging near metal with moderate b-values and volumetric coverage in clinically feasible scan times, using diffusion-prepared FSE, was developed. Application of root-flipped Shinnar-Le Roux refocusing pulses permits preparation of a high spectral bandwidth, which improves imaging times by reducing the number of excitations required to cover the desired spectral range. B1 sensitivity is reduced by employing an excitation that satisfies the CPMG condition in the preparation. The gradient waveform used for ADC quantification was shown theoretically and experimentally to be insensitive to background gradients. Application in vivo demonstrates complementary contrast to conventional multispectral acquisitions and improved visualization compared to DW-EPI. Finally, a computationally efficient tool for optimizing the Cramér-Rao Lower Bound (CRLB) of quantitative sequences without using approximations or an analytical expression of the signal was developed. Automatic differentiation was applied to Bloch simulations and used to optimize several quantitative sequences without the need for approximations or an analytical expression. The results were validated with in vivo measurements and comparisons to prior art. The CRLB of the Magnetic Resonance Fingerprinting (MRF) sequence, which has a complicated analytical formulation, was optimized using automatic differentiation.
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 | 2022; ©2022 |
| Publication date | 2022; 2022 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Lee, Philip Kenneth |
|---|---|
| Degree supervisor | Hargreaves, Brian Andrew |
| Thesis advisor | Hargreaves, Brian Andrew |
| Thesis advisor | Nishimura, Dwight George |
| Thesis advisor | Pauly, John (John M.) |
| 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 | Philip Kenneth Lee. |
|---|---|
| Note | Submitted to the Department of Electrical Engineering. |
| Thesis | Thesis Ph.D. Stanford University 2022. |
| Location | https://purl.stanford.edu/cw045gq7614 |
Access conditions
- Copyright
- © 2022 by Philip Kenneth Lee
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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