A Magnetic Vector Potential-Based Linear Predictor to Increase Peripheral Nerve Stimulation Thresholds in Gradient Coil Design

Motivation: The linear predictor incorporating a coupled electromagnetic-neurodynamic model shows reliable estimation of Peripheral Nerve Stimulation (PNS) thresholds, which is important for gradient coil design. However, the computational complexity and long computation time within the coupled model leads to difficulties in application. Goal(s): We proposed a simplified predictor based on the spatial distribution of magnetic fields, circumventing complexity of the coupled model. Approach: The magnetic vector potential was employed to form a simplified predictor, serving as a constraint for PNS-optimized gradient coil design. Results: With the simplified predictor, the optimized coil achieved an 84% increase in PNS threshold at a 10% inductance penalty. Impact: Based on the magnetic vector potential, the proposed predictor enabled the simplified evaluation of PNS thresholds through magnetic field spatial distribution. In addition, the proposed method facilitated PNS optimization in gradient coil design.