Krueger Felix, Aigner Christoph Stefan, Lutz Max, Riemann Layla Tabea, Degenhardt Katja, Hadjikiriakos Kimon, Zimmermann Felix Frederik, Hammernik Kerstin, Schulz-Menger Jeanette, Schaeffter Tobias, Schmitter Sebastian
Physikalisch-Technische Bundesanstalt, Berlin, Germany.
Einstein Centre Digital Future, Technische Universität Berlin, Biomedical Engineering, Berlin, Germany.
Magn Reson Med. 2025 Apr;93(4):1700-1711. doi: 10.1002/mrm.30359. Epub 2024 Oct 27.
This study investigates the feasibility of using complex-valued neural networks (NNs) to estimate quantitative transmit magnetic RF field (B ) maps from multi-slice localizer scans with different slice orientations in the human head at 7T, aiming to accelerate subject-specific B -calibration using parallel transmission (pTx).
Datasets containing channel-wise B -maps and corresponding multi-slice localizers were acquired in axial, sagittal, and coronal orientation in 15 healthy subjects utilizing an eight-channel pTx transceiver head coil. Training included five-fold cross-validation for four network configurations: used transversal, sagittal, coronal data, and was trained on all slice orientations. The resulting maps were compared to B -reference scans using different quality metrics. The proposed network was applied in-vivo at 7T in two unseen test subjects using dynamic kt-point pulses.
Predicted B -maps demonstrated a high similarity with measured B -maps across multiple orientations. The estimation matched the reference with a mean relative error in the magnitude of (2.70 ± 2.86)% and mean absolute phase difference of (6.70 ± 1.99)° for transversal, (1.82 ± 0.69)% and (4.25 ± 1.62)° for sagittal ( ), as well as (1.33 ± 0.27)% and (2.66 ± 0.60)° for coronal slices ( ) considering brain tissue. trained on all orientations enables a robust prediction of B -maps across different orientations. Achieving a homogenous excitation over the whole brain for an in-vivo application displayed the approach's feasibility.
This study demonstrates the feasibility of utilizing complex-valued NNs to estimate multi-slice B -maps in different slice orientations from localizer scans in the human brain at 7T.
本研究探讨使用复值神经网络（NNs）从7T人体头部不同切片方向的多层定位扫描中估计定量发射磁射频场（B）图的可行性，旨在利用并行传输（pTx）加速特定受试者的B校准。
使用八通道pTx收发器头部线圈，在15名健康受试者中获取包含逐通道B图和相应多层定位器的数据集，扫描方向为轴向、矢状和冠状。训练包括对四种网络配置进行五折交叉验证：使用横向、矢状、冠状数据，以及在所有切片方向上进行训练。使用不同的质量指标将所得图与B参考扫描进行比较。所提出的网络在7T下对两名未见过的测试受试者进行体内应用，使用动态kt点脉冲。
预测的B图在多个方向上与测量的B图显示出高度相似性。对于横向切片，估计值与参考值匹配，幅度的平均相对误差为（2.70±2.86）%，平均绝对相位差为（6.70±1.99）°；对于矢状切片（），分别为（1.82±0.69）%和（4.25±1.62）°；对于冠状切片（），考虑脑组织时分别为（1.33±0.27）%和（2.66±0.60）°。在所有方向上进行训练的能够对不同方向的B图进行稳健预测。在体内应用中实现全脑均匀激发显示了该方法的可行性。
本研究证明了利用复值神经网络从7T人脑定位扫描中估计不同切片方向的多层B图的可行性。
