Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, 361005, China.
Center for Brain Imaging Science and Technology, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, 310027, Zhejiang, China.
Eur Radiol. 2023 Jul;33(7):4938-4948. doi: 10.1007/s00330-023-09417-2. Epub 2023 Jan 24.
To develop a real-time abdominal T mapping method without requiring breath-holding or respiratory-gating.
The single-shot multiple overlapping-echo detachment (MOLED) pulse sequence was employed to achieve free-breathing T mapping of the abdomen. Deep learning was used to untangle the non-linear relationship between the MOLED signal and T mapping. A synthetic data generation flow based on Bloch simulation, modality synthesis, and randomization was proposed to overcome the inadequacy of real-world training set.
The results from simulation and in vivo experiments demonstrated that our method could deliver high-quality T mapping. The average NMSE and R values of linear regression in the digital phantom experiments were 0.0178 and 0.9751. Pearson's correlation coefficient between our predicted T and reference T in the phantom experiments was 0.9996. In the measurements for the patients, real-time capture of the T value changes of various abdominal organs before and after contrast agent injection was realized. A total of 33 focal liver lesions were detected in the group, and the mean and standard deviation of T values were 141.1 ± 50.0 ms for benign and 63.3 ± 16.0 ms for malignant lesions. The coefficients of variance in a test-retest experiment were 2.9%, 1.2%, 0.9%, 3.1%, and 1.8% for the liver, kidney, gallbladder, spleen, and skeletal muscle, respectively.
Free-breathing abdominal T mapping is achieved in about 100 ms on a clinical MRI scanner. The work paved the way for the development of real-time dynamic T mapping in the abdomen.
• MOLED achieves free-breathing abdominal T mapping in about 100 ms, enabling real-time capture of T value changes due to CA injection in abdominal organs. • Synthetic data generation flow mitigates the issue of lack of sizable abdominal training datasets.
开发无需屏气或呼吸门控的实时腹部 T 映射方法。
采用单次多重叠回波分离（MOLED）脉冲序列实现自由呼吸腹部 T 映射。利用深度学习来解开 MOLED 信号与 T 映射之间的非线性关系。提出了一种基于布洛赫模拟、模态合成和随机化的合成数据生成流程，以克服真实训练集不足的问题。
模拟和体内实验结果表明，我们的方法可以提供高质量的 T 映射。数字体模实验中线性回归的平均 NMSE 和 R 值分别为 0.0178 和 0.9751。体模实验中我们预测的 T 值与参考 T 值之间的皮尔逊相关系数为 0.9996。在对患者的测量中，实现了对比剂注射前后各种腹部器官 T 值变化的实时采集。在该组中总共检测到 33 个局灶性肝病变，良性病变和恶性病变的 T 值平均值和标准差分别为 141.1±50.0 ms 和 63.3±16.0 ms。在一项测试-重测实验中，肝脏、肾脏、胆囊、脾脏和骨骼肌的 T 值的变异系数分别为 2.9%、1.2%、0.9%、3.1%和 1.8%。
在临床 MRI 扫描仪上约 100ms 实现了自由呼吸腹部 T 映射。这项工作为腹部实时动态 T 映射的发展铺平了道路。
• MOLED 在约 100ms 内实现自由呼吸腹部 T 映射，能够实时捕获腹部器官因 CA 注射引起的 T 值变化。
• 合成数据生成流程缓解了缺乏大量腹部训练数据集的问题。
