Optimization of MRI Gradient Coils With Explicit Peripheral Nerve Stimulation Constraints

Abstract

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.

Electric field calculation and peripheral nerve stimulation prediction for head and body gradient coils

“…One has been mentioned already: a sampling bias that would occur if the anatomic dimensions of the small cohort of subjects used in any given experimental study are not consistent with a random sample from the Gaussian‐distributed population that our calculations used. Another factor is that our simplified body models cannot possibly represent the full anatomic and physiologic complexity of the real human body; this factor has motivated a number of groups to use more detailed body models for switched‐magnetic‐field‐induced E‐field calculations 15,25,34,39‐45 . Despite this, our finding that PNS threshold predictions can be made with an average absolute error of ~5% for a typical body gradient coil and ~20% for a typical head gradient coil means that our simplified body models have important application in PNS and head gradient research.…”

“…E‐field‐based PNS prediction, if performed according to regulatory guidelines, meets safety standards such as International Electrotechnical Commission (IEC) 60601‐2‐33 14 . More importantly, rapid prediction of PNS thresholds from calculated E‐fields could provide the necessary input for PNS‐optimal gradient coil design, 15 a new and important development in gradient engineering.…”

Minimum electric‐field gradient coil design: Theoretical limits and practical guidelines

“…The concept of constraining/minimizing E-fields or controlling PNS performance by intelligent gradient design has been proposed in previous literature [18][19][20][21][22][23][24][25] and has inspired the present work. We believe our work contributes significant new insights and practical solutions to the problem of PNSoptimal gradient design.…”

“…Prior work has proposed the incorporation of E‐field calculations or PNS estimation into the gradient design process 18‐25 . Expanding on these efforts, we integrate our computationally efficient E‐field methods into a gradient design algorithm that is then used to evaluate both theoretical and practical limits of minimum E‐field gradient design.…”

“…17 Prior work has proposed the incorporation of E-field calculations or PNS estimation into the gradient design process. [18][19][20][21][22][23][24][25] Expanding on these efforts, we integrate our computationally efficient E-field methods into a gradient design algorithm that is then used to evaluate both theoretical and practical limits of minimum E-field gradient design. We believe our approach is an improvement for finding globally optimal solutions over a wide range of anatomical dimensions and gradient topologies, and for providing a solid conceptual understanding of the theoretical limits of minimum E-field gradient design.…”