C.J. Gorter Center for High Field MRI, Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands.
Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands.
Magn Reson Med. 2022 Jul;88(1):464-475. doi: 10.1002/mrm.29215. Epub 2022 Mar 28.
Parallel RF transmission (PTx) is one of the key technologies enabling high quality imaging at ultra-high fields (≥7T). Compliance with regulatory limits on the local specific absorption rate (SAR) typically involves over-conservative safety margins to account for intersubject variability, which negatively affect the utilization of ultra-high field MR. In this work, we present a method to generate a subject-specific body model from a single T1-weighted dataset for personalized local SAR prediction in PTx neuroimaging at 7T.
Multi-contrast data were acquired at 7T (N = 10) to establish ground truth segmentations in eight tissue types. A 2.5D convolutional neural network was trained using the T1-weighted data as input in a leave-one-out cross-validation study. The segmentation accuracy was evaluated through local SAR simulations in a quadrature birdcage as well as a PTx coil model.
The network-generated segmentations reached Dice coefficients of 86.7% ± 6.7% (mean ± SD) and showed to successfully address the severe intensity bias and contrast variations typical to 7T. Errors in peak local SAR obtained were below 3.0% in the quadrature birdcage. Results obtained in the PTx configuration indicated that a safety margin of 6.3% ensures conservative local SAR estimates in 95% of the random RF shims, compared to an average overestimation of 34% in the generic "one-size-fits-all" approach.
A subject-specific body model can be automatically generated from a single T1-weighted dataset by means of deep learning, providing the necessary inputs for accurate and personalized local SAR predictions in PTx neuroimaging at 7T.
并行射频传输（PTx）是在超高场（≥7T）实现高质量成像的关键技术之一。为了符合局部比吸收率（SAR）的规定限制，通常需要采用过度保守的安全裕度来考虑个体间的变异性，这会对超高场磁共振的利用产生负面影响。在这项工作中，我们提出了一种方法，从单个 T1 加权数据集生成个体特异性体模，以便在 7T 的 PTx 神经成像中进行个性化的局部 SAR 预测。
在 7T 上采集多对比度数据（N=10），以建立八种组织类型的真实分割。使用 T1 加权数据作为输入，通过留一法交叉验证研究训练一个 2.5D 卷积神经网络。通过在正交鸟笼和 PTx 线圈模型中的局部 SAR 模拟来评估分割准确性。
网络生成的分割达到了 86.7%±6.7%（均值±标准差）的 Dice 系数，并且成功解决了 7T 中典型的严重强度偏差和对比度变化问题。在正交鸟笼中获得的局部 SAR 峰值误差低于 3.0%。在 PTx 配置中获得的结果表明，与通用的“一刀切”方法平均高估 34%相比，安全裕度为 6.3%可确保在 95%的随机射频补偿中保守的局部 SAR 估计。
通过深度学习，可以从单个 T1 加权数据集自动生成个体特异性体模，为在 7T 的 PTx 神经成像中进行准确和个性化的局部 SAR 预测提供必要的输入。
