Advances In Simulation And Thermography For high Field Mri
Open Access
- Author:
- Cao, Zhipeng
- Graduate Program:
- Bioengineering
- Degree:
- Doctor of Philosophy
- Document Type:
- Dissertation
- Date of Defense:
- April 08, 2013
- Committee Members:
- Qing X Yang, Dissertation Advisor/Co-Advisor
Qing X Yang, Committee Chair/Co-Chair
Jesse Louis Barlow, Committee Member
Thomas Neuberger, Committee Member
William Joseph Weiss, Committee Member
Christopher Collins, Special Member
Mark Griswold, Special Member - Keywords:
- MRI
FDTD
Simulation
Hyperthermia
Reconstruction
Compressed Sensing - Abstract:
- High field MRI systems can benefit from increased signal-to-noise ratio (SNR) but face challenges of decreased homogeneity in signal intensity across images and increased patient heating. Currently, engineering studies for high field MRI involve modeling of human subjects and RF coils and calculating the MR relevant electromagnetic fields, as well as collecting experimental MR data to validate the simulation prediction. Presented here is a computer-based MRI system simulator developed to solve the Bloch equation with consideration of accurate electromagnetic fields calculated with finite-difference-time-domain (FDTD) method. It is demonstrated that the MRI system simulator can simulate many realistic MR phenomena. It bridges the gap between field simulation and experimental MR imaging, and can potentially facilitate the validation of new ideas by MR researchers. By utilizing the system simulator and an FDTD solver, an analysis of high field MRI performance at up to 14 Tesla with current standard transmission and reception methods has been performed. It is found that for imaging of the human head, depending on the imaging sequence used high field MRI could have more-than-linear increase in SNR and less-than-quadratic increase in energy dissipation in the subject. Finally, in order to explore the possibility of patient-specific temperature monitoring to ensure safety due to increased power deposition at high field, a novel compressed sensing reconstruction technique is presented to improve the acquisition speed of proton resonance frequency shift thermography.