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

Tan, Ek Tsoon; Hua, Yaping; Fiveland, Eric; Vermilyea, Mark E; Piel, Joseph E.; Park, Keith J; Ho, Vincent B.; Foo, Thomas K.F.

doi:10.1002/mrm.27909

Cited by 27 publications

(54 citation statements)

References 36 publications

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“…The PNS of the MAGNUS gradient was similar to that of C3T-HG, as expected. In a separate set of experiments reported elsewhere, 40 the PNS threshold of the MAGNUS gradient was similar to that reported for C3T-HG. 26 In that separate study in 20 healthy subjects, a slightly lower rheobase of 46.9 ± 5.3 T/s but larger chronaxie time of 766 ± 76 µs, as compared to C3T-HG (rheobase = 52.2 T/s, chronaxie = 611 µs) were measured.…”

Section: Discussionsupporting

confidence: 80%

Highly efficient head‐only magnetic field insert gradient coil for achieving simultaneous high gradient amplitude and slew rate at 3.0T (MAGNUS) for brain microstructure imaging

Foo

Tan

Vermilyea

et al. 2019

Magnetic Resonance in Med

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Purpose To develop a highly efficient magnetic field gradient coil for head imaging that achieves 200 mT/m and 500 T/m/s on each axis using a standard 1 MVA gradient driver in clinical whole‐body 3.0T MR magnet. Methods A 42‐cm inner diameter head‐gradient used the available 89‐ to 91‐cm warm bore space in a whole‐body 3.0T magnet by increasing the radial separation between the primary and the shield coil windings to 18.6 cm. This required the removal of the standard whole‐body gradient and radiofrequency coils. To achieve a coil efficiency ~4× that of whole‐body gradients, a double‐layer primary coil design with asymmetric x‐y axes, and symmetric z‐axis was used. The use of all‐hollow conductor with direct fluid cooling of the gradient coil enabled ≥50 kW of total heat dissipation. Results This design achieved a coil efficiency of 0.32 mT/m/A, allowing 200 mT/m and 500 T/m/s for a 620 A/1500 V driver. The gradient coil yielded substantially reduced echo spacing, and minimum repetition time and echo time. In high b = 10,000 s/mm2 diffusion, echo time (TE) < 50 ms was achieved (>50% reduction compared with whole‐body gradients). The gradient coil passed the American College of Radiology tests for gradient linearity and distortion, and met acoustic requirements for nonsignificant risk operation. Conclusions Ultra‐high gradient coil performance was achieved for head imaging without substantial increases in gradient driver power in a whole‐body 3.0T magnet after removing the standard gradient coil. As such, any clinical whole‐body 3.0T MR system could be upgraded with 3‐4× improvement in gradient performance for brain imaging.

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Section: Discussionsupporting

confidence: 80%

Highly efficient head‐only magnetic field insert gradient coil for achieving simultaneous high gradient amplitude and slew rate at 3.0T (MAGNUS) for brain microstructure imaging

Foo

Tan

Vermilyea

et al. 2019

Magnetic Resonance in Med

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“…For MAGNUS at 3‐MVA per‐axis, the performance can potentially reach 400 mT/m and 900 T/m/s, which could provide 150 Hz OGSE at b = 615 s/mm 2 , or 100 Hz at b = 2250 s/mm 2 . The primary limitation to attaining higher frequencies or b‐values or frequencies is PNS, which in the MAGNUS head system could potentially be mitigated by careful positioning of the patient or by improved gradient designs optimized for PNS . Clinical applications for high‐frequency OGSE with ultra‐strong gradients will be focused on probing of human tissue microstructure at smaller diffusion times or length scales than previously possible, which may lead to the discovery of novel imaging biomarkers, either combined with or separate from other diffusion tensor or kurtosis metrics.…”

Section: Discussionmentioning

confidence: 99%

“…Rather, ξ could be assumed to be that limited by PNS based on the maximum gradient amplitude of the system. Specifically, the PNS parameters used for body gradient were

Δ G_{min, body} = 23.5

mT/m,

S R_{min, body}

= 70.3 T/m/s, and for MAGNUS head‐gradient were

Δ G_{min, head} = 111

mT/m,

S R_{min, head}

= 145 T/m/s . Hence, for each frequency and b‐value evaluated with sinusoids, the corresponding b‐value for trapezoids could be derived.…”

Section: Methodsmentioning

confidence: 99%

“…In this work, OGSE was evaluated on the microstructure anatomy gradient for neuroimaging with ultrafast scanning (MAGNUS), which is a head‐gradient that is able to simultaneously achieve high gradient amplitudes and slew rates of G max = 200 mT/m and SR max = 500 T/m/s respectively with similar high slew rate threshold for PNS as the Compact 3T head gradient . The effects of gradient performance on OGSE were first evaluated in simulations to determine the achievable b‐values and frequencies.…”

Section: Introductionmentioning

confidence: 99%

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Oscillating diffusion‐encoding with a high gradient‐amplitude and high slew‐rate head‐only gradient for human brain imaging

Tan

Shih

Mitra

et al. 2020

Magnetic Resonance in Med

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Purpose:We investigate the importance of high gradient-amplitude and high slew-rate on oscillating gradient spin echo (OGSE) diffusion imaging for human brain imaging and evaluate human brain imaging with OGSE on the MAGNUS head-gradient insert (200 mT/m amplitude and 500 T/m/s slew rate). Methods: Simulations with cosine-modulated and trapezoidal-cosine OGSE at various gradient amplitudes and slew rates were performed. Six healthy subjects were imaged with the MAGNUS gradient at 3T with OGSE at frequencies up to 100 Hz and b = 450 s/mm 2 . Comparisons were made against standard pulsed gradient spin echo (PGSE) diffusion in vivo and in an isotropic diffusion phantom. Results: Simulations show that to achieve high frequency and b-value simultaneously for OGSE, high gradient amplitude, high slew rates, and high peripheral nerve stimulation limits are required. A strong linear trend for increased diffusivity (mean: 8-19%, radial: 9-27%, parallel: 8-15%) was observed in normal white matter with OGSE (20 Hz to 100 Hz) as compared to PGSE. Linear fitting to frequency provided excellent correlation, and using a short-range disorder model provided radial longterm diffusivities of D ∞,MD = 911 ± 72 µm 2 /s, D ∞,PD = 1519 ± 164 µm 2 /s, and D ∞,RD = 640 ± 111 µm 2 /s and correlation lengths of l c,MD = 0.802 ± 0.156 µm, l c,PD = 0.837 ± 0.172 µm, and l c,RD = 0.780 ± 0.174 µm. Diffusivity changes with OGSE frequency were negligible in the phantom, as expected. Conclusion: The high gradient amplitude, high slew rate, and high peripheral nerve stimulation thresholds of the MAGNUS head-gradient enables OGSE acquisition for in vivo human brain imaging. K E Y W O R D S diffusion imaging, head-gradient, microstructure | 951 TAN eT Al.

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“…High-performance research systems for imaging human brain connectivity and microstructure have been developed with G max of 300 mT/m (McNab et al, 2013) or slew rate of 1200 T/m/s (Weiger et al, 2018). Local head gradient coils have the potential to reduce E-fields in the body/torso and to elicit peripheral nerve stimulation (PNS) threshold for in vivo human imaging (Chronik and Rutt, 2001;Zhang et al, 2003;Tan et al, 2019). Advancements in functionalized anatomical models with nerve trajectories Neufeld et al, 2018) coupled with EM and neurodynamic simulations (Davids et al, 2017(Davids et al, , 2019 have the potential for designing high-performance MRI gradient coils.…”

Section: Discussionmentioning

confidence: 99%

Predicting in vivo MRI Gradient-Field Induced Voltage Levels on Implanted Deep Brain Stimulation Systems Using Neural Networks

Erturk

Panken

Conroy

et al. 2020

Front. Hum. Neurosci.

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Introduction: MRI gradient-fields may induce extrinsic voltage between electrodes and conductive neurostimulator enclosure of implanted deep brain stimulation (DBS) systems, and may cause unintended stimulation and/or malfunction. Electromagnetic (EM) simulations using detailed anatomical human models, therapy implant trajectories, and gradient coil models can be used to calculate clinically relevant induced voltage levels. Incorporating additional anatomical human models into the EM simulation library can help to achieve more clinically relevant and accurate induced voltage levels, however, adding new anatomical human models and developing implant trajectories is time-consuming, expensive and not always feasible.Methods: MRI gradient-field induced voltage levels are simulated in six adult human anatomical models, along clinically relevant DBS implant trajectories to generate the dataset. Predictive artificial neural network (ANN) regression models are trained on the simulated dataset. Leave-one-out cross validation is performed to assess the performance of ANN regressors and quantify model prediction errors.Results: More than 180,000 unique gradient-induced voltage levels are simulated. ANN algorithm with two fully connected layers is selected due to its superior generalizability compared to support vector machine and tree-based algorithms in this particular application. The ANN regression model is capable of producing thousands of gradient-induced voltage predictions in less than a second with mean-squared-error less than 200 mV. Conclusion:We have integrated machine learning (ML) with computational modeling and simulations and developed an accurate predictive model to determine MRI gradientfield induced voltage levels on implanted DBS systems.

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

Cited by 27 publications

References 36 publications

Highly efficient head‐only magnetic field insert gradient coil for achieving simultaneous high gradient amplitude and slew rate at 3.0T (MAGNUS) for brain microstructure imaging

Highly efficient head‐only magnetic field insert gradient coil for achieving simultaneous high gradient amplitude and slew rate at 3.0T (MAGNUS) for brain microstructure imaging

Oscillating diffusion‐encoding with a high gradient‐amplitude and high slew‐rate head‐only gradient for human brain imaging

Predicting in vivo MRI Gradient-Field Induced Voltage Levels on Implanted Deep Brain Stimulation Systems Using Neural Networks

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