Date of Graduation
8-2014
Document Type
Dissertation (PhD)
Program Affiliation
Medical Physics
Degree Name
Doctor of Philosophy (PhD)
Advisor/Committee Chair
John D. Hazle, Ph.D.
Committee Member
David Fuentes, Ph.D.
Committee Member
Valen Johnson, Ph.D.
Committee Member
Jingfei Ma, Ph.D.
Committee Member
R. Jason Stafford, Ph.D.
Committee Member
Nikolaos V. Tsekos, Ph.D.
Abstract
During magnetic resonance (MR)-guided thermal therapies, proton resonance frequency shift (PRFS) based MR temperature imaging can quantitatively monitor tissue temperature changes. It is widely known that the PRFS technique is easily perturbed by tissue motion, tissue susceptibility changes, magnetic field drift, and modality–dependent applicator induced artifacts. Due to recent advances in computational algorithms and hardware, much more powerful statistical analysis methods are becoming realizable in the real-time processing environment. To this end, my dissertation research focused on the development, validation, and implementation of stochastic data-driven processing techniques to increase the robustness of MR temperature monitoring during thermal therapies. MR temperature imaging was demonstrated to achieve a high degree of accuracy in damage predictions during rapid ablation procedures. In the event of temperature imaging data loss, a Kalman filtered MR temperature imaging algorithm using an uncorrelated, sparse covariance matrix for a Pennes bioheat model was developed to predict temperature in regions of missing or erroneous measurement. Temperature predictions were demonstrated to be accurate, while being less computationally expensive than the dense covariance matrix used in standard Kalman filtering. A second approach developed and investigated was the use of a Gaussian process for MR temperature imaging to allow for an accurate probabilistic extrapolation of the background phase. The technique demonstrated reliable temperature estimates in the presence of unwanted background field changes. The Gaussian process algorithm was also implemented to forecast temperature using a limited number of a priori temperature images. The performance of these proposed approaches was validated in simulations, ex vivo, and in vivo. These techniques allow for a full probabilistic prediction and an estimate of the uncertainty that provide a statistical model for MR temperature imaging. In conclusion, I have developed two novel approaches to MR temperature imaging post-processing and demonstrated the feasibility of application of these stochastic, data-driven models developed to improve the robustness of MR-guidance during thermal therapies and potentially enhance the safety and efficacy of treatment.
Keywords
magnetic resonance temperature imaging, thermal therapy