William Allyn Grissom1, Viola Rieke, John Pauly2, Nathan McDannold3, Kim Butts-Pauly
1Electrical Engineering and Radiology, Stanford University, Stanford, CA, USA; 2Electrical Engineering, Stanford University, Stanford, CA, USA; 3Harvard Medical School, Brigham & Women's Hospital, Boston, MA, USA
We present two new regularized iterative methods for estimating temperature change images from multicoil PRF-shift MR thermometry data. The first method uses baseline images acquired before thermal therapy, while the second is a reference-less method. Compared to conventional thermometry techniques, the new method is statistically motivated, generalizes to multicoil acquisitions, and is robust to noise. In addition, unlike conventional reference-less methods, the new reference-less method also does not require the user to track the heated region. We validate the new methods in simulations and experiments.