  Current MRI super-resolution (SR) methods only use existing contrasts
acquired from typical clinical sequences as input for the neural network (NN).
In turbo spin echo sequences (TSE) the sequence parameters can have a strong
influence on the actual resolution of the acquired image and have consequently
a considera-ble impact on the performance of the NN. We propose a
known-operator learning approach to perform an end-to-end optimization of MR
sequence and neural net-work parameters for SR-TSE. This MR-physics-informed
training procedure jointly optimizes the radiofrequency pulse train of a proton
density- (PD-) and T2-weighted TSE and a subsequently applied convolutional
neural network to predict the corresponding PDw and T2w super-resolution TSE
images. The found radiofrequency pulse train designs generate an optimal signal
for the NN to perform the SR task. Our method generalizes from the
simulation-based optimi-zation to in vivo measurements and the acquired
physics-informed SR images show higher correlation with a time-consuming
segmented high-resolution TSE sequence compared to a pure network training
approach.
