Anatomical measurements correlate with individual magnetostimulation thresholds for kHz‐range homogeneous magnetic fields

Abstract

Purpose: Magnetostimulation, also known as peripheral nerve stimulation (PNS), is the dominant safety constraint in magnetic resonance imaging (MRI) for the gradient magnetic fields that operate around 0.1-1 kHz, and for the homogeneous drive field in magnetic particle imaging (MPI) that operates around 10-150 kHz. Previous studies did not report correlations between anatomical measures and magnetostimulation thresholds for the gradient magnetic fields in MRI. In contrast, a strong linear correlation was shown between the thresholds and the inverse of body part size in MPI. Yet, the effects of other anatomical measures on the thresholds for the drive field remain unexplored. Here, we investigate the effects of fat percentage on magnetostimulation thresholds for kHz-range homogeneous magnetic fields such as the drive field in MPI, with the ultimate goal of predicting subject-specific thresholds based on simple anatomical measures. Methods: Human subject experiments were performed on the upper arms of 10 healthy male subjects (age: 26 AE 2 yr) to determine magnetostimulation thresholds. Experiments were repeated three times for each subject, with brief resting periods between repetitions. Using a solenoidal magnetostimulation coil, a homogeneous magnetic field at 25 kHz with 100 ms pulse duration was applied at 4-s intervals, while the subject reported stimulation via a mouse click. To determine the thresholds, individual subject responses were fitted to a cumulative distribution function modeled by a sigmoid curve. Next, anatomical images of the upper arms of the subjects were acquired on a 3 T MRI scanner. A two-point Dixon method was used to obtain separate images of water and fat tissues, from which several anatomical measures were derived: the effective outer radius of the upper arm, the effective inner radius (i.e., the muscle radius), and fat percentage. Pearson's correlation coefficient was used to assess the relationship between the threshold and anatomical measures. This statistical analysis was repeated after factoring out the expected effects of body part size. An updated model for threshold prediction is provided, where in addition to scaling in proportion with the inverse of the outer radius, the threshold has an affine dependence on fat percentage. Results: A strong linear correlation (r = 0.783, P < 0.008) was found between magnetostimulation threshold and fat percentage, and the correlation became stronger after factoring out the effects of outer radius (r = 0.839, P < 0.003). While considering body part size alone did not explain any significant variance in measured thresholds (P > 0.398), the updated model that also incorporates fat percentage yielded substantially improved threshold predictions with R 2 = 0.654 (P < 0.001). Conclusions: This work shows for the first time that fat percentage strongly correlates with magnetostimulation thresholds for kHz-range homogenous magnetic fields such as the drive field in MPI, and that the correlations get even stronger after factoring out the effects of b...

Optimization of MRI Gradient Coils With Explicit Peripheral Nerve Stimulation Constraints

“…Instead, the winding optimizer uses macroscopic strategies for PNS reduction, such as bending the Bfield away from the torso and redirecting it to smaller body parts (such as the extremities or the head) where they lead to smaller E-field amplitudes, and therefore reduced PNS propensity. In reality, there are of course many sources of PNS threshold variability among subjects, including body shapes, tissue composition (such as body fat percentage) [19,59], varying dielectric tissue properties, and anatomical and physiological details of the nerve fibers (such as level of myelination, node of Ranvier spacing, etc) [19]. We hypothesize that this variability does not significantly impair the PNS improvements of our optimized windings, as we are aiming for relative threshold improvements (i.e., increasing the PNS threshold averaged over a large population).…”