论文标题
剪切的深控制：深神经网络二维脉冲设计具有振幅约束层
Clipped DeepControl: deep neural network two-dimensional pulse design with an amplitude constraint layer
论文作者
论文摘要
磁共振成像中使用的先进射频脉冲设计最近通过深入学习（卷积）神经网络和增强学习。对于二维选择性的射频脉冲，（卷积）神经网络脉冲预测时间（几毫秒）比传统的最佳控制计算快三个数量级。网络脉冲来自能够补偿B0和B+1个领域的扫描依赖性不均匀性的监督培训。不幸的是，尽管在训练中使用的最佳对照脉冲，但该网络在测试子集中呈现不可忽略的脉冲振幅超过了。在这里，我们使用定制的剪辑层扩展了卷积神经网络，该网络完全消除了脉冲振幅过大的风险，同时保留了补偿不均匀野外条件的能力。
Advanced radio-frequency pulse design used in magnetic resonance imaging has recently been demonstrated with deep learning of (convolutional) neural networks and reinforcement learning. For two-dimensionally selective radio-frequency pulses, the (convolutional) neural network pulse prediction time (few milliseconds) was in comparison more than three orders of magnitude faster than the conventional optimal control computation. The network pulses were from the supervised training capable of compensating scan-subject dependent inhomogeneities of B0 and B+1 fields. Unfortunately, the network presented with a non-negligible percentage of pulse amplitude overshoots in the test subset, despite the optimal control pulses used in training were fully constrained. Here, we have extended the convolutional neural network with a custom-made clipping layer that completely eliminates the risk of pulse amplitude overshoots, while preserving the ability to compensate the inhomogeneous field conditions.
