Mitigation of peripheral nerve stimulation (PNS) in MRI

7.项目概要/摘要 MRI中的周围神经刺激（PNS）是由通过神经刺激的开关引起的电场引起的。 梯度线圈，这可能导致刺激身体中最大的神经（大直径神经 比小的更容易兴奋）。使用当前一代的Gmax=80 mT/m，Smax=200 T/m/s全身MRI梯度在很大程度上受PNS而不是放大器功率的限制， 机械问题或散热和特殊线圈，例如Gmax=300 mT/m，Smax=200 T/m/s “MGH Connectome”线圈只能在其操作参数空间的一小部分内完全使用。 影响这些PNS限制将允许更快的成像，更高的分辨率和减少的失真， 常规用于研究和临床的许多序列用于头/颈以及身体成像， 如EPI、DWI、bSSFP、RARE和PROPELLER。仅头部（HO）梯度插入具有更高的 阈值，但它们的最新一代也是PNS有限的。此外，大多数神经影像学研究 研究和几乎所有的临床研究都使用全身（WB）梯度系统。 在这个程序中，我们开发了一个具有显式PNS约束的梯度设计工具， 通过优化WB和HO设计的实验测试来验证PNS的好处。国家- 最先进的边界元（BEM）-流函数（SF）方法，用于设计 梯度线圈优化了受到电、机械和热约束的磁场， 但忽略了主要限制因素PNS。虽然存在设计经验法则，但PNS并不直接 在设计步骤中。相反，PNS是在构建线圈原型后评估的。 志愿者这是一种昂贵且缓慢的方法，仅允许最小的PNS缓解迭代。在 这个建议，我们建立在我们的工作建模磁刺激全身周围神经 模型考虑到：i）线圈导线图案，ii）感应线圈的详细形状， iii）刺激效果对组织边界的相对电场的依赖性， 电场和神经之间的取向，iv）非线性神经动力学及其差异 属性取决于类（运动，体感或自主）和分支距离 CNS。我们的初步结果表明，我们可以提高PNS阈值的2倍，WB和1.7倍， HO设计。成本是线性误差（5%）和电感（仅32%）的适度增加 WB设计所需）。这表明，缠绕模式本质上包含自由度 可以支持实质性的PNS改进，如果一个人有工具，以发现他们在 设计阶段。因此，我们将我们的PNS分析纳入行业标准的BEM-SF设计 优化框架，并通过构建和测试PNS中的最佳线圈设计来验证我们的结果 健康志愿者的阈值研究。

7. Project Summary/Abstract Peripheral nerve stimulation (PNS) in MRI results from electric fields induced by the switching of gradient coil, which may result in stimulation of the largest nerves in the body (large diameter nerves are easier to excite than small ones). The use of current generation of Gmax=80 mT/m, Smax=200 T/m/s whole-body MRI gradients is largely constrained by PNS rather than amplifier power, mechanical issues or heat removal and specialty coils such as the Gmax=300 mT/m, Smax=200 T/m/s “MGH Connectome” coil can only be fully used within a fraction of its operational parameter space. Impacting these PNS limitations will allow faster imaging, higher resolution and reduced distortions in many sequences routinely used for research and in the clinic for head/neck as well as body imaging, such as EPI, DWI, bSSFP, RARE and PROPELLER. Head-only (HO) gradient inserts have higher thresholds but their latest generation are also PNS limited. Additionally, most neuroimaging research studies and nearly all clinical studies use whole-body (WB) gradient systems. In this program, we develop a gradient design tool with explicit PNS constraints and validate the PNS benefits by experimental tests of optimized WB and HO designs. The state-of- the-art boundary element (BEM)-stream function (SF) approach for designing the winding patterns of gradient coils optimizes the magnetic field subject to electrical, mechanical and thermal constraints, but ignores the primary limiting factor; PNS. Although design rules-of-thumb exist, PNS is not directly incorporated in the design step. Instead, PNS is assessed after construction of a coil prototype on volunteers. This is a costly and slow approach that allows only minimal PNS mitigation iteration. In this proposal, we build on our work modeling magneto-stimulation in full-body peripheral nerve models which takes into account: i) the coil wire pattern, ii) the detailed shaping of the induced electric fields by the tissue boundaries, iii) the dependence of the stimulation effect on the relative orientation between electric field and nerves, iv) the non-linear nerve dynamics and their differing properties depending on class (motor, somatosensory or autonomic) and branching distance from the CNS. Our preliminary results indicate that we can increase PNS thresholds by 2X for WB and 1.7X for HO designs. The cost is a moderate increase of the linearity error (5%) and inductance (32%, only required for WB designs). This shows that winding patterns intrinsically contain degrees-of-freedom that can support substantial PNS improvements if one has the tools to uncover them during the design phase. We therefore incorporate our PNS analysis into an industry-standard BEM-SF design optimization framework and validate our results by building and testing the best coil designs in a PNS threshold study of healthy volunteers.