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      Peripheral nerve stimulation limits of a high amplitude and slew rate magnetic field gradient coil for neuroimaging

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          Abstract

          To establish peripheral nerve stimulation (PNS) thresholds for an ultra-high performance magnetic field gradient sub-system (simultaneous 200 mT/m gradient amplitude and 500 T/m/s gradient slew rate; 1-MVA per-axis: referred to as MAGNUS) designed for neuroimaging with asymmetric transverse gradients and 42-cm inner diameter, and to determine PNS threshold dependencies on gender, age, patient positioning within the gradient sub-system, and anatomical landmarks. The MAGNUS head-gradient was installed in a whole-body 3.0T scanner with a custom 16-rung bird-cage transmit/receive radiofrequency coil compatible with phased-array receiver brain coils. Twenty adult subjects (10 male, mean±s.d. age=40.4±11.1 years) underwent the imaging and PNS study. The tests were repeated by displacing subject positions by 2–4 cm in the superior-inferior and anterior-posterior directions. The X-axis (left-right) yielded mostly facial stimulation, with mean Δ G min =111±6 mT/m, chronaxie=766±76 μsec. The Z-axis (superior-inferior) yielded mostly chest/shoulder stimulation (123±7 mT/m, 620±62 μsec). Y-axis (anterior-posterior) stimulation was negligible. X- and Z-axes thresholds tended to increase with age, and there was negligible dependency with gender. Translation in the inferior and posterior directions tended to increase X- and Z-axes thresholds, respectively. Electric field simulations showed good agreement with the PNS results. Imaging at MAGNUS gradient performance with increased PNS threshold provided a 35% reduction in noise-to-diffusion contrast as compared to whole-body performance (80 mT/m gradient amplitude, 200 T/m/sec gradient slew rate). The PNS threshold of MAGNUS is significantly higher than that for whole-body gradients, which allows for diffusion gradients with short rise-times (under 1 msec), important for interrogating brain microstructure length-scales.

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          Most cited references2

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          Is Open Access

          Predicting Magnetostimulation Thresholds in the Peripheral Nervous System using Realistic Body Models

          Rapid switching of applied magnetic fields in the kilohertz frequency range in the human body induces electric fields powerful enough to cause Peripheral Nerve Stimulation (PNS). PNS has become one of the main constraints on the use of high gradient fields for fast imaging with the latest MRI gradient technology. In recent MRI gradients, the applied fields are powerful enough that PNS limits their application in fast imaging sequences like echo-planar imaging. Application of Magnetic Particle Imaging (MPI) to humans is similarly PNS constrained. Despite its role as a major constraint, PNS considerations are only indirectly incorporated in the coil design process, mainly through using the size of the linear region as a proxy for PNS thresholds or by conducting human experiments after constructing coil prototypes. We present for the first time, a framework to simulate PNS thresholds for realistic coil geometries to directly address PNS in the design process. Our PNS model consists of an accurate body model for electromagnetic field simulations, an atlas of peripheral nerves, and a neurodynamic model to predict the nerve responses to imposed electric fields. With this model, we were able to reproduce measured PNS thresholds of two leg/arm solenoid coils with good agreement.
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            Model-based denoising in diffusion-weighted imaging using generalized spherical deconvolution.

            Diffusion MRI often suffers from low signal-to-noise ratio, especially for high b-values. This work proposes a model-based denoising technique to address this limitation.
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              Author and article information

              Contributors
              Journal
              Magnetic Resonance in Medicine
              Magn Reson Med
              Wiley
              0740-3194
              1522-2594
              August 20 2019
              January 2020
              August 06 2019
              January 2020
              : 83
              : 1
              : 352-366
              Affiliations
              [1 ]GE Research Niskayuna New York
              [2 ]Uniformed Services University of the Health Sciences Bethesda Maryland
              [3 ]Walter Reed National Military Medical Center Bethesda Maryland
              Article
              10.1002/mrm.27909
              6778706
              31385628
              bb2ed724-16bd-4e21-bfaa-027cedaf04df
              © 2020

              http://onlinelibrary.wiley.com/termsAndConditions#am

              http://onlinelibrary.wiley.com/termsAndConditions#vor

              http://doi.wiley.com/10.1002/tdm_license_1.1

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