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

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.

[1]  Niels Kuster,et al.  Functionalized anatomical models for EM-neuron Interaction modeling , 2016, Physics in medicine and biology.

[2]  Emre Kopanoglu,et al.  A Simple Analytical Expression for the Gradient Induced Potential on Active Implants During MRI , 2012, IEEE Transactions on Biomedical Engineering.

[3]  F Rattay,et al.  Stimulation of the Human Lumbar Spinal Cord With Implanted and Surface Electrodes: A Computer Simulation Study , 2010, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[4]  C. McIntyre,et al.  Modeling the excitability of mammalian nerve fibers: influence of afterpotentials on the recovery cycle. , 2002, Journal of neurophysiology.

[5]  R Bowtell,et al.  Analytic calculations of the E‐fields induced by time‐varying magnetic fields generated by cylindrical gradient coils , 2000, Magnetic resonance in medicine.

[6]  D. McRobbie,et al.  Thresholds for biological effects of time-varying magnetic fields. , 1984, Clinical physics and physiological measurement : an official journal of the Hospital Physicists' Association, Deutsche Gesellschaft fur Medizinische Physik and the European Federation of Organisations for Medical Physics.

[7]  O. Gandhi,et al.  Specific absorption rates and induced current densities for an anatomy‐based model of the human for exposure to time‐varying magnetic fields of MRI , 1999, Magnetic resonance in medicine.

[8]  Niels Kuster,et al.  Development of a new generation of high-resolution anatomical models for medical device evaluation: the Virtual Population 3.0 , 2014, Physics in medicine and biology.

[9]  Warren M Grill,et al.  Analysis of the quasi-static approximation for calculating potentials generated by neural stimulation , 2008, Journal of neural engineering.

[10]  Nicholas T. Carnevale,et al.  ModelDB: A Database to Support Computational Neuroscience , 2004, Journal of Computational Neuroscience.

[11]  Stuart Crozier,et al.  Calculation of electric fields induced by body and head motion in high-field MRI. , 2003, Journal of magnetic resonance.

[12]  Akimasa Hirata,et al.  Multi-scale simulations predict responses to non-invasive nerve root stimulation , 2014, Journal of neural engineering.

[13]  H. H. Pennes Analysis of tissue and arterial blood temperatures in the resting human forearm. 1948. , 1948, Journal of applied physiology.

[14]  F. Rattay Analysis of models for extracellular fiber stimulation , 1989, IEEE Transactions on Biomedical Engineering.

[15]  Niels Kuster,et al.  The Virtual Family—development of surface-based anatomical models of two adults and two children for dosimetric simulations , 2010, Physics in medicine and biology.

[16]  A. Huxley,et al.  The action potential in the myelinated nerve fibre of Xenopus laevis as computed on the basis of voltage clamp data , 1964, The Journal of physiology.

[17]  J. Patrick Reilly,et al.  Sensory Effects of Transient Electrical Stimulation - Evaluation with a Neuroelectric Model , 1985, IEEE Transactions on Biomedical Engineering.

[18]  Akimasa Hirata,et al.  Computational analysis shows why transcranial alternating current stimulation induces retinal phosphenes , 2013, Journal of neural engineering.

[19]  Theodoros Samaras,et al.  Thermal Tissue Damage Model Analyzed for Different Whole‐Body SAR and Scan Durations for Standard MR Body Coils , 2014, Magnetic resonance in medicine.

[20]  Maria A. Stuchly,et al.  Peripheral nerve stimulation by gradient switching fields in magnetic resonance imaging , 2004, IEEE Transactions on Biomedical Engineering.