Literature DB >> 29637905

Realistic modeling of deep brain stimulation implants for electromagnetic MRI safety studies.

Bastien Guerin1, Peter Serano, Maria Ida Iacono, Todd M Herrington, Alik S Widge, Darin D Dougherty, Giorgio Bonmassar, Leonardo M Angelone, Lawrence L Wald.   

Abstract

We propose a framework for electromagnetic (EM) simulation of deep brain stimulation (DBS) patients in radiofrequency (RF) coils. We generated a model of a DBS patient using post-operative head and neck computed tomography (CT) images stitched together into a 'virtual CT' image covering the entire length of the implant. The body was modeled as homogeneous. The implant path extracted from the CT data contained self-intersections, which we corrected automatically using an optimization procedure. Using the CT-derived DBS path, we built a model of the implant including electrodes, helicoidal internal conductor wires, loops, extension cables, and the implanted pulse generator. We also built four simplified models with straight wires, no extension cables and no loops to assess the impact of these simplifications on safety predictions. We simulated EM fields induced by the RF birdcage body coil in the body model, including at the DBS lead tip at both 1.5 Tesla (64 MHz) and 3 Tesla (123 MHz). We also assessed the robustness of our simulation results by systematically varying the EM properties of the body model and the position and length of the DBS implant (sensitivity analysis). The topology correction algorithm corrected all self-intersection and curvature violations of the initial path while introducing minimal deformations (open-source code available at http://ptx.martinos.org/index.php/Main_Page). The unaveraged lead-tip peak SAR predicted by the five DBS models (0.1 mm resolution grid) ranged from 12.8 kW kg-1 (full model, helicoidal conductors) to 43.6 kW kg-1 (no loops, straight conductors) at 1.5 T (3.4-fold variation) and 18.6 kW kg-1 (full model, straight conductors) to 73.8 kW kg-1 (no loops, straight conductors) at 3 T (4.0-fold variation). At 1.5 T and 3 T, the variability of lead-tip peak SAR with respect to the conductivity ranged between 18% and 30%. Variability with respect to the position and length of the DBS implant ranged between 9.5% and 27.6%.

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Mesh:

Year:  2018        PMID: 29637905      PMCID: PMC5935557          DOI: 10.1088/1361-6560/aabd50

Source DB:  PubMed          Journal:  Phys Med Biol        ISSN: 0031-9155            Impact factor:   3.609


  38 in total

1.  MR imaging-related heating of deep brain stimulation electrodes: in vitro study.

Authors:  Daniel A Finelli; Ali R Rezai; Paul M Ruggieri; Jean A Tkach; John A Nyenhuis; Greg Hrdlicka; Ashwini Sharan; Jorge Gonzalez-Martinez; Paul H Stypulkowski; Frank G Shellock
Journal:  AJNR Am J Neuroradiol       Date:  2002 Nov-Dec       Impact factor: 3.825

Review 2.  Uncovering the mechanism(s) of action of deep brain stimulation: activation, inhibition, or both.

Authors:  Cameron C McIntyre; Marc Savasta; Lydia Kerkerian-Le Goff; Jerrold L Vitek
Journal:  Clin Neurophysiol       Date:  2004-06       Impact factor: 3.708

Review 3.  Mechanisms of deep brain stimulation.

Authors:  Todd M Herrington; Jennifer J Cheng; Emad N Eskandar
Journal:  J Neurophysiol       Date:  2015-10-28       Impact factor: 2.714

4.  Reduction of the radiofrequency heating of metallic devices using a dual-drive birdcage coil.

Authors:  Yigitcan Eryaman; Esra Abaci Turk; Cagdas Oto; Oktay Algin; Ergin Atalar
Journal:  Magn Reson Med       Date:  2012-05-10       Impact factor: 4.668

5.  Evaluation of the RF heating of a generic deep brain stimulator exposed in 1.5 T magnetic resonance scanners.

Authors:  Eugenia Cabot; Tom Lloyd; Andreas Christ; Wolfgang Kainz; Mark Douglas; Gregg Stenzel; Steve Wedan; Niels Kuster
Journal:  Bioelectromagnetics       Date:  2012-10-11       Impact factor: 2.010

6.  Feasibility of using linearly polarized rotating birdcage transmitters and close-fitting receive arrays in MRI to reduce SAR in the vicinity of deep brain simulation implants.

Authors:  Laleh Golestanirad; Boris Keil; Leonardo M Angelone; Giorgio Bonmassar; Azma Mareyam; Lawrence L Wald
Journal:  Magn Reson Med       Date:  2016-04-05       Impact factor: 4.668

7.  Designing passive MRI-safe implantable conducting leads with electrodes.

Authors:  Paul A Bottomley; Ananda Kumar; William A Edelstein; Justin M Allen; Parag V Karmarkar
Journal:  Med Phys       Date:  2010-07       Impact factor: 4.071

8.  Bilateral deep-brain stimulation of the globus pallidus in primary generalized dystonia.

Authors:  Marie Vidailhet; Laurent Vercueil; Jean-Luc Houeto; Pierre Krystkowiak; Alim-Louis Benabid; Philippe Cornu; Christelle Lagrange; Sophie Tézenas du Montcel; Didier Dormont; Sylvie Grand; Serge Blond; Olivier Detante; Bernard Pillon; Claire Ardouin; Yves Agid; Alain Destée; Pierre Pollak
Journal:  N Engl J Med       Date:  2005-02-03       Impact factor: 91.245

9.  Reduction of magnetic resonance imaging-related heating in deep brain stimulation leads using a lead management device.

Authors:  Kenneth B Baker; Jean Tkach; John D Hall; John A Nyenhuis; Frank G Shellock; Ali R Rezai
Journal:  Neurosurgery       Date:  2005-10       Impact factor: 4.654

10.  Deep brain stimulation of the ventral capsule/ventral striatum for treatment-resistant depression.

Authors:  Donald A Malone; Darin D Dougherty; Ali R Rezai; Linda L Carpenter; Gerhard M Friehs; Emad N Eskandar; Scott L Rauch; Steven A Rasmussen; Andre G Machado; Cynthia S Kubu; Audrey R Tyrka; Lawrence H Price; Paul H Stypulkowski; Jonathon E Giftakis; Mark T Rise; Paul F Malloy; Stephen P Salloway; Benjamin D Greenberg
Journal:  Biol Psychiatry       Date:  2008-10-08       Impact factor: 13.382

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  14 in total

1.  The 'virtual DBS population': five realistic computational models of deep brain stimulation patients for electromagnetic MR safety studies.

Authors:  Bastien Guerin; Maria Ida Iacono; Mathias Davids; Darin Dougherty; Leonardo M Angelone; Lawrence L Wald
Journal:  Phys Med Biol       Date:  2019-02-04       Impact factor: 3.609

Review 2.  Improving Safety of MRI in Patients with Deep Brain Stimulation Devices.

Authors:  Alexandre Boutet; Clement T Chow; Keshav Narang; Gavin J B Elias; Clemens Neudorfer; Jürgen Germann; Manish Ranjan; Aaron Loh; Alastair J Martin; Walter Kucharczyk; Christopher J Steele; Ileana Hancu; Ali R Rezai; Andres M Lozano
Journal:  Radiology       Date:  2020-06-23       Impact factor: 11.105

3.  The effect of simulation strategies on prediction of power deposition in the tissue around electronic implants during magnetic resonance imaging.

Authors:  Bach T Nguyen; Julie Pilitsis; Laleh Golestanirad
Journal:  Phys Med Biol       Date:  2020-09-16       Impact factor: 3.609

4.  Machine learning-based prediction of MRI-induced power absorption in the tissue in patients with simplified deep brain stimulation lead models.

Authors:  Jasmine Vu; Bach T Nguyen; Bhumi Bhusal; Justin Baraboo; Joshua Rosenow; Ulas Bagci; Molly G Bright; Laleh Golestanirad
Journal:  IEEE Trans Electromagn Compat       Date:  2021-09-30       Impact factor: 2.036

5.  Risk assessment of copper-containing contraceptives: the impact for women with implanted intrauterine devices during clinical MRI and CT examinations.

Authors:  Wiebke Neumann; Tanja Uhrig; Matthias Malzacher; Verena Kossmann; Lothar R Schad; Frank G Zoellner
Journal:  Eur Radiol       Date:  2018-11-19       Impact factor: 5.315

6.  Parallel transmission to reduce absorbed power around deep brain stimulation devices in MRI: Impact of number and arrangement of transmit channels.

Authors:  Bastien Guerin; Leonardo M Angelone; Darin Dougherty; Lawrence L Wald
Journal:  Magn Reson Med       Date:  2019-08-07       Impact factor: 4.668

Review 7.  Virtual clinical trials in medical imaging: a review.

Authors:  Ehsan Abadi; William P Segars; Benjamin M W Tsui; Paul E Kinahan; Nick Bottenus; Alejandro F Frangi; Andrew Maidment; Joseph Lo; Ehsan Samei
Journal:  J Med Imaging (Bellingham)       Date:  2020-04-11

8.  Individualized SAR calculations using computer vision-based MR segmentation and a fast electromagnetic solver.

Authors:  Eugene Milshteyn; Georgy Guryev; Angel Torrado-Carvajal; Elfar Adalsteinsson; Jacob K White; Lawrence L Wald; Bastien Guerin
Journal:  Magn Reson Med       Date:  2020-07-08       Impact factor: 4.668

9.  Modeling radiofrequency responses of realistic multi-electrode leads containing helical and straight wires.

Authors:  Mikhail Kozlov; Marc Horner; Wolfgang Kainz
Journal:  MAGMA       Date:  2019-11-19       Impact factor: 2.310

10.  Advancing Regulatory Science With Computational Modeling for Medical Devices at the FDA's Office of Science and Engineering Laboratories.

Authors:  Tina M Morrison; Pras Pathmanathan; Mariam Adwan; Edward Margerrison
Journal:  Front Med (Lausanne)       Date:  2018-09-25
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