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.

翻译：当前的MRI超分辨率（SR）方法仅使用从典型临床序列获取的现有对比度作为神经网络（NN）的输入。在涡轮自旋回波序列（TSE）中，序列参数对采集图像的实际分辨率具有显著影响，进而对NN的性能产生重要影响。我们提出了一种已知算子学习方法，用于对SR-TSE的MR序列和神经网络参数进行端到端优化。这种基于MR物理信息的训练过程联合优化了质子密度（PD）加权和T2加权TSE的射频脉冲序列，以及随后应用的卷积神经网络，以预测相应的PD加权和T2加权超分辨TSE图像。所得到的射频脉冲序列设计为NN执行SR任务生成了最优信号。我们的方法从基于模拟的优化推广到体内测量，与纯网络训练方法相比，所获得的物理信息SR图像与耗时的高分辨率分段TSE序列具有更高的相关性。