Xu Sijie, Zong Shenyan, Mei Chang-Sheng, Shen Guofeng, Zhao Yueran, Wang He
Biomedical Instrument Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
Med Phys. 2025 Jul;52(7):e17909. doi: 10.1002/mp.17909. Epub 2025 May 31.
Proton resonance frequency (PRF)-based magnetic resonance (MR) thermometry plays a critical role in thermal ablation therapies through focused ultrasound (FUS). For clinical applications, accurate and rapid temperature feedback is essential to ensure both the safety and effectiveness of these treatments.
This work aims to improve temporal resolution in dynamic MR temperature map reconstructions using an enhanced deep-learning method, thereby supporting the real-time monitoring required for effective FUS treatments.
Five classical neural network architectures-cascade net, complex-valued U-Net, shift window transformer for MRI, real-valued U-Net, and U-Net with residual blocks-along with training-optimized methods were applied to reconstruct temperature maps from 2-fold and 4-fold undersampled k-space data. The training enhancements included pre-training/training-phase data augmentations, knowledge distillation, and a novel amplitude-phase decoupling loss function. Phantom and ex vivo tissue heating experiments were conducted using a FUS transducer. Ground truth was the complex MR images with accurate temperature changes, and datasets were manually undersampled to simulate such acceleration here. Separate testing datasets were used to evaluate real-time performance and temperature accuracy. Furthermore, our proposed deep learning-based rapid reconstruction approach was validated on a clinical dataset obtained from patients with uterine fibroids, demonstrating its clinical applicability.
Acceleration factors of 1.9 and 3.7 were achieved for 2× and 4× k-space under samplings, respectively. The deep learning-based reconstruction using ResUNet incorporating the four optimizations, showed superior performance. For 2-fold acceleration, the RMSE of temperature map patches were 0.89°C and 1.15°C for the phantom and ex vivo testing datasets, respectively. The DICE coefficient for the 43°C isotherm-enclosed regions was 0.81, and the Bland-Altman analysis indicated a bias of -0.25°C with limits of agreement of ±2.16°C. In the 4-fold under-sampling case, these evaluation metrics showed approximately a 10% reduction in accuracy. Additionally, the DICE coefficient measuring the overlap between the reconstructed temperature maps (using the optimized ResUNet) and the ground truth, specifically in regions where the temperature exceeded the 43°C threshold, were 0.77 and 0.74 for the 2× and 4× under-sampling scenarios, respectively.
This study demonstrates that deep learning-based reconstruction significantly enhances the accuracy and efficiency of MR thermometry, particularly in the context of FUS-based clinical treatments for uterine fibroids. This approach could also be extended to other applications such as essential tremor and prostate cancer treatments where MRI-guided FUS plays a critical role.
基于质子共振频率（PRF）的磁共振（MR）测温在聚焦超声（FUS）热消融治疗中起着关键作用。对于临床应用而言，准确且快速的温度反馈对于确保这些治疗的安全性和有效性至关重要。
本研究旨在使用一种增强的深度学习方法提高动态MR温度图重建中的时间分辨率，从而支持有效的FUS治疗所需的实时监测。
将五种经典神经网络架构——级联网络、复值U-Net、用于MRI的移位窗口变换器、实值U-Net以及带有残差块的U-Net——与训练优化方法一起应用于从2倍和4倍欠采样的k空间数据重建温度图。训练增强措施包括预训练/训练阶段的数据增强、知识蒸馏以及一种新颖的幅度-相位解耦损失函数。使用FUS换能器进行了体模和离体组织加热实验。真实情况是具有准确温度变化的复MR图像，并且在此处手动对数据集进行欠采样以模拟这种加速。使用单独的测试数据集来评估实时性能和温度准确性。此外，我们提出的基于深度学习的快速重建方法在从子宫肌瘤患者获得的临床数据集上得到了验证，证明了其临床适用性。
在2倍和4倍k空间欠采样情况下，分别实现了1.9和3.7的加速因子。采用包含四种优化的ResUNet进行基于深度学习的重建表现出卓越的性能。对于2倍加速，体模和离体测试数据集的温度图补丁的均方根误差（RMSE）分别为0.89°C和1.15°C。43°C等温线包围区域的DICE系数为0.81，Bland-Altman分析表明偏差为-0.25°C，一致性界限为±2.16°C。在4倍欠采样情况下，这些评估指标的准确性下降了约10%。此外，测量重建温度图（使用优化的ResUNet）与真实情况之间重叠的DICE系数，特别是在温度超过43°C阈值的区域，在2倍和4倍欠采样情况下分别为0.77和0.74。
本研究表明基于深度学习的重建显著提高了MR测温的准确性和效率，特别是在基于FUS的子宫肌瘤临床治疗背景下。这种方法还可以扩展到其他应用，如原发性震颤和前列腺癌治疗，其中MRI引导的FUS起着关键作用。
