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.

翻译：基于质子共振频率（PRF）的磁共振测温技术对于聚焦超声热消融治疗至关重要。本研究旨在通过改进的深度学习方法，提升动态磁共振温度图重建的时间分辨率。研究将训练优化方法与五种经典神经网络应用于2倍和4倍欠采样的k空间数据，以重建温度图。增强的训练模块包括离线/在线数据增强、知识蒸馏以及幅度-相位解耦损失函数。加热实验分别通过聚焦超声换能器在仿体和离体组织上进行。这些数据被手动欠采样以模拟加速过程，并在我们的方法中进行训练以获得重建模型。另外获取了十余组测试数据集，用于评估实时性能和温度精度。对于2倍和4倍k空间欠采样策略，分别实现了1.9和3.7的加速因子，其中基于ResUNet的深度学习重建表现尤为出色。在2倍加速场景下，仿体和离体测试数据集的温度图局部区域的均方根误差分别为0.888摄氏度和1.145摄氏度。43摄氏度等温线所围温度区域的DICE值为0.809，Bland-Altman分析显示偏差为-0.253摄氏度，一致性界限为±2.16摄氏度。在4倍欠采样情况下，这些评估值下降了约10%。本研究表明，基于深度学习的重建方法能显著提升临床聚焦超声热疗中磁共振测温的准确性和效率。