  Proton resonance frequency (PRF) based MR thermometry is essential for
focused ultrasound (FUS) thermal ablation therapies. This work aims to enhance
temporal resolution in dynamic MR temperature map reconstruction using an
improved deep learning method. The training-optimized methods and five
classical neural networks were applied on the 2-fold and 4-fold under-sampling
k-space data to reconstruct the temperature maps. The enhanced training modules
included offline/online data augmentations, knowledge distillation, and the
amplitude-phase decoupling loss function. The heating experiments were
performed by a FUS transducer on phantom and ex vivo tissues, respectively.
These data were manually under-sampled to imitate acceleration procedures and
trained in our method to get the reconstruction model. The additional dozen or
so testing datasets were separately obtained for evaluating the real-time
performance and temperature accuracy. Acceleration factors of 1.9 and 3.7 were
found for 2 times and 4 times k-space under-sampling strategies and the
ResUNet-based deep learning reconstruction performed exceptionally well. In
2-fold acceleration scenario, the RMSE of temperature map patches provided the
values of 0.888 degree centigrade and 1.145 degree centigrade on phantom and ex
vivo testing datasets. The DICE value of temperature areas enclosed by 43
degree centigrade isotherm was 0.809, and the Bland-Altman analysis showed a
bias of -0.253 degree centigrade with the apart of plus or minus 2.16 degree
centigrade. In 4 times under-sampling case, these evaluating values decreased
by approximately 10%. This study demonstrates that deep learning-based
reconstruction can significantly enhance the accuracy and efficiency of MR
thermometry for clinical FUS thermal therapies.
