A Simple Analytical Expression for the Gradient Induced Potential on Active Implants During MRI

Türk, Esra Abacı; Kopanoğlu, Emre; Güney, Şevin; Bugdayci, K. E.; Ider, Y.Z.; Ertürk, Vakur B.; Atalar, Ergin

doi:10.1109/tbme.2012.2212190

Cited by 8 publications

(6 citation statements)

References 20 publications

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“…The overall simulated duration was 10 ms. This curve shape is equivalent to the shape of the derivative of the gradient coil scaling function in Turk et al (2012) and Bowtell and Bowley (2000), and is suitable for the modeling of gradient switching induced currents; f 2 ( t ) uses a single 2 ms pulse, similar to what was employed by Reilly and Diamant (2011b), to determine the rheobase; f 3 ( t ) uses a true biphasic pulse with a 0.2 ms initial zero scaling interval, followed by a 0.1 ms positive amplitude rectangular pulse and an equivalent negative amplitude pulse immediately after.…”

Section: Methodsmentioning

confidence: 99%

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Investigation of assumptions underlying current safety guidelines on EM-induced nerve stimulation

et al. 2016

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An intricate network of a variety of nerves is embedded within the complex anatomy of the human body. Although nerves are shielded from unwanted excitation, they can still be stimulated by external electromagnetic sources that induce strongly non-uniform field distributions. Current exposure safety standards designed to limit unwanted nerve stimulation are based on a series of explicit and implicit assumptions and simplifications. This paper demonstrates the applicability of functionalized anatomical phantoms with integrated coupled electromagnetic and neuronal dynamics solvers for investigating the impact of magnetic resonance exposure on nerve excitation within the full complexity of the human anatomy. The impact of neuronal dynamics models, temperature and local hot-spots, nerve trajectory and potential smoothing, anatomical inhomogeneity, and pulse duration on nerve stimulation was evaluated. As a result, multiple assumptions underlying current safety standards are questioned. It is demonstrated that coupled EM-neuronal dynamics modeling involving realistic anatomies is valuable to establish conservative safety criteria.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Investigation of assumptions underlying current safety guidelines on EM-induced nerve stimulation

et al. 2016

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“…Modeling showed that significantly higher electric fields can be induced near metallic implant by fast‐switching gradient fields, which may increase the risk of PNS . Further theoretical work has been presented to calculate electric field induced by switching gradient field on implant . One study specifically modeled the electric field induced near pacemaker leads, suggesting potential hazardous situation of unintended stimulation .…”

Section: Em Modeling For Implant Safety In Mrimentioning

confidence: 99%

Electromagnetic computation and modeling in MRI

Chen

Steckner

2017

Medical Physics

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Electromagnetic (EM) computational modeling is used extensively during the development of a Magnetic Resonance Imaging (MRI) scanner, its installation, and use. MRI, which relies on interactions between nuclear magnetic moments and the applied magnetic fields, uses a range of EM tools to optimize all of the magnetic fields required to produce the image. The main field magnet is designed to exacting specifications but challenges in manufacturing, installation, and use require additional tools to maintain target operational performance. The gradient magnetic fields, which provide the primary signal localization mechanism, are designed under another set of complex design trade-offs which include conflicting imaging performance specifications and patient physiology. Gradients are largely impervious to external influences, but are also used to enhance main field operational performance. The radiofrequency (RF) magnetic fields, which are used to elicit the signals fundamental to the MR image, are a challenge to optimize for a host of reasons that include patient safety, image quality, cost optimization, and secondary signal localization capabilities. This review outlines these issues and the EM modeling used to optimize MRI system performance.

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“…To tackle this issue, a mathematical model can allow to calculate the induced voltage for simple geometries. Simplified analytical expressions were proposed [64] to calculate the electric field induced by a linear gradient field for an active implanted medical device. In this study, it is assumed that the time-varying field is formed by infinitely long cylindrical gradient coil.…”

Section: Induced Voltagementioning

confidence: 99%

A Review of Numerical Simulation and Analytical Modeling for Medical Devices Safety in MRI

Kabil¹,

Belguerras²,

Trattnig³

et al. 2016

Yearb Med Inform

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A Simple Analytical Expression for the Gradient Induced Potential on Active Implants During MRI

Cited by 8 publications

References 20 publications

Investigation of assumptions underlying current safety guidelines on EM-induced nerve stimulation

Investigation of assumptions underlying current safety guidelines on EM-induced nerve stimulation

Electromagnetic computation and modeling in MRI

A Review of Numerical Simulation and Analytical Modeling for Medical Devices Safety in MRI

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