Minimizing peripheral nerve stimulation in gradient coil design for head MRI systems using a local field-based linear...

Area of Science:

• Medical Imaging Physics
• Biomedical Engineering
• Computational Electromagnetics

Background:

• Gradient coils in head MRI systems can cause peripheral nerve stimulation (PNS).
• Accurate prediction of PNS thresholds is crucial for designing safer MRI systems.
• Current methods for PNS estimation can be computationally intensive.

Purpose of the Study:

• To develop a novel linear predictor for estimating PNS thresholds in head MRI gradient coil design.
• To minimize peripheral nerve stimulation (PNS) by optimizing gradient coil parameters.
• To enable fast and accurate PNS estimation based on local magnetic fields.

Main Methods:

• Developed a linear threshold predictor approximating the activating function (AF) using local magnetic field components.
• Modeled the reciprocal of PNS thresholds as a linear expression of the approximated AF.
• Fit predictor coefficients using a least-squares method on a dataset from reference gradient coils and incorporated it into a coil design framework.

Main Results:

• The predictor achieved an AF estimation error below 1% and maximum PNS threshold prediction errors of 4.7% (X), 1.7% (Y), and 2.7% (Z).
• Incorporating the predictor increased PNS thresholds by 119% with only a 15% increase in coil inductance.
• Maintained a maximum linearity deviation of 5% in the gradient coil design.

Conclusions:

• The proposed predictor accurately relates local magnetic fields to PNS thresholds.
• This tool provides efficient PNS threshold evaluation for gradient coil design.
• Enables the development of head MRI systems with minimized PNS for improved patient safety.