Improved characterization of sequence-specific peripheral nerve stimulation (PNS) thresholds for rapid on-scanner monitoring

Motivation: The on-scanner PNS monitor must estimate each sequence’s stimulation potential. The current SAFE model is overly conservative, unnecessarily restricting gradient performance by up to 1.8X. Goal(s): Develop a PNS monitor model that rapidly and accurately characterizes a sequence’s PNS thresholds. Approach: We propose a model (SAFE2) closely inspired by the mechanisms of PNS to capture critical aspects such as E-field cancelations from different gradient axes and extend the training data using detailed PNS modeling reflecting a more diverse set of waveforms. Results: SAFE2 improves PNS-prediction accuracy by 2X compared to SAFE, boosting usable image encoding performance by up to 30% without hardware changes. Impact: PNS restricts the usable protocol parameters of EPI, bSSFP, Radial-GRE, etc., yielding suboptimal imaging performance. The current PNS monitoring approach (SAFE) is very conservative, therefore, we propose an improved model yielding up to 30% gradient performance boost without hardware modification.