Keywords: Bioeffects & Magnetic Fields, Safety, Peripheral Nerve Stimulation (PNS), gradient coils, fast imaging
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.
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