Mitigation of Peripheral-Nerve Stimulation with Arbitrary Gradient Waveform Design for Diffusion-Weighted MRI

Motivation: Peripheral nerve stimulation (PNS) can be problematic on ultra-high-performance gradients systems, especially during diffusion encoding. We developed a gradient waveform optimization approach to mitigate PNS. Goal(s): Our goal was to investigate the minimum achievable TE (TEmin) using arbitrary gradient waveform design while mitigating PNS for brain, liver and heart DWI. Approach: We used gradient optimization (GrOpt) to design gradient waveforms for TEmin of different protocols, then imaged a phantom and a volunteer with a brain DWI protocol implemented with Pulseq. Results: GrOpt consistently reduces TEs compared to conventional approaches and avoids PNS. Image quality was the same in phantom and in vivo studies. Impact: Ultra high-performance gradient systems increase diffusion sensitivity and resolution, but their application can be constrained due to peripheral nerve stimulation (PNS). We used open-source gradient optimization (GrOpt) to design arbitrary gradient waveforms for minimum time that mitigate PNS.