Mitigation of peripheral nerve stimulation (PNS) in MRI

7.项目摘要/摘要 周围神经刺激(PNS)在MRI中是由开关产生的电场引起的 梯度线圈，可能会刺激体内最大的神经(大直径神经) 比小的更容易激动)。使用当代GMAX=80mT/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，如Gmax=300Mt/m，Smax=200T/m/S “MGH Connectome”线圈只能在其运行参数空间的一小部分内充分使用。 影响这些PNS限制将允许更快的成像、更高的分辨率和更少的失真 许多序列通常用于研究和临床的头部/颈部以及身体成像， 如EPI、DWI、bSSFP、REARE和PRESPEER。仅头部(HO)渐变镶件具有更高的 门槛，但他们的最新一代也是有限的PNS。此外，大多数神经成像研究 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，我们开发了一个带有显式PNS约束的梯度设计工具 通过优化的WB和HO设计的实验测试，验证了PNS的好处。国家-- 用边界元(BEM)-流函数(SF)方法设计绕组图案 梯度线圈优化了受电、机械和热约束的磁场， 但忽略了主要的限制因素：PNS。尽管存在设计经验法则，但PNS并不直接 结合在设计步骤中。取而代之的是，在构建线圈原型之后对PNS进行评估 志愿者。这是一种昂贵且缓慢的方法，只允许最低限度的PNS缓解迭代。在……里面 这一建议，我们建立在我们的工作基础上，在全身周围神经建模磁刺激 模型考虑了：i)线圈导线图案，ii)感应器的详细成形 组织边界的电场，III)刺激效应对相对电场的依赖关系 电场与神经的定向，IV)非线性神经动力学及其区别 属性取决于类别(运动、躯体感觉或自主神经)和与 中枢神经系统。我们的初步结果表明，我们可以将WB的PNS阈值提高2倍，将PNS阈值提高1.7倍 对于Ho设计。成本是线性误差(5%)和电感(仅32%)的适度增加 WB设计所需的)。这表明缠绕模式本质上包含自由度 这可以支持PNS的实质性改进，如果人们有工具在 设计阶段。因此，我们将PNS分析整合到行业标准的BEM-SF设计中 优化框架，并通过在PNS中构建和测试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.