Peripheral Nerve Stimulation (PNS) limits the acquisition rate of Magnetic Resonance Imaging data for fast sequences employing powerful gradient systems. The PNS characteristics are currently assessed after the coil design phase in experimental stimulation studies using constructed coil prototypes. This makes it difficult to find design modifications that can reduce PNS. Here, we demonstrate a direct approach for incorporation of PNS effects into the coil optimization process. Knowledge about the interactions between the applied magnetic fields and peripheral nerves allows the optimizer to identify coil solutions that minimize PNS while satisfying the traditional engineering constraints. We compare the simulated thresholds of PNS-optimized body and head gradients to conventional designs, and find an up to 2-fold reduction in PNS propensity with moderate penalties in coil inductance and field linearity, potentially doubling the image encoding performance that can be safely used in humans. The same framework may be useful in designing and operating magneto- and electro-stimulation devices.

中文翻译：

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具有显式周围神经刺激约束的 MRI 梯度线圈的优化。


周围神经刺激 (PNS) 限制了采用强大梯度系统的快速序列的磁共振成像数据的采集速率。目前，PNS 特性是在使用构建的线圈原型进行实验刺激研究的线圈设计阶段之后进行评估的。这使得很难找到可以减少 PNS 的设计修改。在这里，我们演示了一种将三七总皂苷效应纳入线圈优化过程的直接方法。了解所施加的磁场和周围神经之间的相互作用使优化器能够识别线圈解决方案，在满足传统工程约束的同时最大限度地减少 PNS。我们将 PNS 优化的身体和头部梯度的模拟阈值与传统设计进行比较，发现 PNS 倾向降低了 2 倍，同时线圈电感和场线性度受到适度的惩罚，可能使可安全使用的图像编码性能提高一倍在人类中。相同的框架可用于设计和操作磁刺激和电刺激设备。