Abstract #2441

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

Ariel J Hannum^1,2,3,4, Michael Loecher^1,2,3, Kawin Setsompop^1,5, and Daniel B Ennis^1,2,3

¹Department of Radiology, Stanford University, Stanford, CA, United States, ²Division of Radiology, Veterans Administration Health Care System, Palo Alto, CA, United States, ³Cardiovascular Institute, Stanford University, Stanford, CA, United States, ⁴Department of Bioengineering, Stanford University, Stanford, CA, United States, ⁵Department of Electrical Engineering, Stanford University, Stanford, CA, United States

Synopsis

Keywords: Diffusion Acquisition, Gradients, Peripheral Nerve Stimulation

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 (TE_min) 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 TE_min 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.

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