A novel approach of applying deep reinforcement learning to an RF pulse design is introduced. This method, which is referred to as DeepRFSLR, is designed to minimize the peak amplitude or, equivalently, minimize the pulse duration of a multiband refocusing pulse generated by the Shinar Le-Roux (SLR) algorithm. In the method, the root pattern of SLR polynomial, which determines the RF pulse shape, is optimized by iterative applications of deep reinforcement learning and greedy tree search. When tested for the designs of the multiband pulses with three and seven slices, DeepRFSLR demonstrated improved performance compared to conventional methods, generating shorter duration RF pulses in shorter computational time. In the experiments, the RF pulse from DeepRFSLR produced a slice profile similar to the minimum-phase SLR RF pulse and the profiles matched to that of the computer simulation. Our approach suggests a new way of designing an RF by applying a machine learning algorithm, demonstrating a “machine-designed” MRI sequence.

中文翻译：

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使用根翻转的深度强化学习设计的 Shinnar-Le Roux 射频脉冲：DeepRFSLR


介绍了一种将深度强化学习应用于射频脉冲设计的新方法。该方法被称为 DeepRFSLR，旨在最小化峰值幅度，或者等效地最小化由 Shinar Le-Roux (SLR) 算法生成的多频带重聚焦脉冲的脉冲持续时间。在该方法中，通过深度强化学习和贪婪树搜索的迭代应用来优化决定射频脉冲形状的SLR多项式的根模式。当对具有三层和七层的多频带脉冲设计进行测试时，DeepRFSLR 表现出与传统方法相比改进的性能，可以在更短的计算时间内生成更短持续时间的射频脉冲。在实验中，来自 DeepRFSLR 的射频脉冲产生了类似于最小相位 SLR 射频脉冲的切片轮廓，并且该轮廓与计算机模拟的轮廓相匹配。我们的方法提出了一种通过应用机器学习算法来设计 RF 的新方法，展示了“机器设计的”MRI 序列。