Literature DB >> 20335090

Analysis of the role of lead resistivity in specific absorption rate for deep brain stimulator leads at 3T MRI.

Leonardo M Angelone1, Jyrki Ahveninen, John W Belliveau, Giorgio Bonmassar.   

Abstract

Magnetic resonance imaging (MRI) on patients with implanted deep brain stimulators (DBSs) can be hazardous because of the antenna-effect of leads exposed to the incident radio-frequency field. This study evaluated electromagnetic field and specific absorption rate (SAR) changes as a function of lead resistivity on an anatomically precise head model in a 3T system. The anatomical accuracy of our head model allowed for detailed modeling of the path of DBS leads between epidermis and the outer table. Our electromagnetic finite difference time domain (FDTD) analysis showed significant changes of 1 g and 10 g averaged SAR for the range of lead resistivity modeled, including highly conductive leads up to highly resistive leads. Antenna performance and whole-head SAR were sensitive to the presence of the DBS leads only within 10%, while changes of over one order of magnitude were observed for the peak 10 g averaged SAR, suggesting that local SAR values should be considered in DBS guidelines. With rho(lead) = rho(copper) , and the MRI coil driven to produce a whole-head SAR without leads of 3.2 W/kg, the 1 g averaged SAR was 1080 W/kg and the 10 g averaged SAR 120 W/kg at the tip of the DBS lead. Conversely, in the control case without leads, the 1 g and 10 g averaged SAR were 0.5 W/kg and 0.6 W/kg, respectively, in the same location. The SAR at the tip of lead was similar with electrically homogeneous and electrically heterogeneous models. Our results show that computational models can support the development of novel lead technology, properly balancing the requirements of SAR deposition at the tip of the lead and power dissipation of the system battery.

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Year:  2010        PMID: 20335090      PMCID: PMC3145199          DOI: 10.1109/TMI.2010.2040624

Source DB:  PubMed          Journal:  IEEE Trans Med Imaging        ISSN: 0278-0062            Impact factor:   10.048


  34 in total

1.  Neurostimulation systems for deep brain stimulation: in vitro evaluation of magnetic resonance imaging-related heating at 1.5 tesla.

Authors:  Ali R Rezai; Daniel Finelli; John A Nyenhuis; Greg Hrdlicka; Jean Tkach; Ashwini Sharan; Paul Rugieri; Paul H Stypulkowski; Frank G Shellock
Journal:  J Magn Reson Imaging       Date:  2002-03       Impact factor: 4.813

2.  Specific absorption rates and induced current densities for an anatomy-based model of the human for exposure to time-varying magnetic fields of MRI.

Authors:  O P Gandhi; X B Chen
Journal:  Magn Reson Med       Date:  1999-04       Impact factor: 4.668

3.  Specific absorption rate as a poor indicator of magnetic resonance-related implant heating.

Authors:  Wolfgang R Nitz; Gerd Brinker; Dirk Diehl; Georg Frese
Journal:  Invest Radiol       Date:  2005-12       Impact factor: 6.016

4.  Dosimetric comparison of the specific anthropomorphic mannequin (SAM) to 14 anatomical head models using a novel definition for the mobile phone positioning.

Authors:  Wolfgang Kainz; Andreas Christ; Tocher Kellom; Seth Seidman; Neviana Nikoloski; Brian Beard; Niels Kuster
Journal:  Phys Med Biol       Date:  2005-07-06       Impact factor: 3.609

5.  Deep brain stimulation for Parkinson's disease.

Authors:  Alim-Louis Benabid; Günther Deuschl; Anthony E Lang; Kelly E Lyons; Ali R Rezai
Journal:  Mov Disord       Date:  2006-06       Impact factor: 10.338

6.  Deep brain stimulation for Parkinson's disease: surgical technique and perioperative management.

Authors:  Andre Machado; Ali R Rezai; Brian H Kopell; Robert E Gross; Ashwini D Sharan; Alim-Louis Benabid
Journal:  Mov Disord       Date:  2006-06       Impact factor: 10.338

7.  Standing-wave and RF penetration artifacts caused by elliptic geometry: an electrodynamic analysis of MRI.

Authors:  J G Sled; G B Pike
Journal:  IEEE Trans Med Imaging       Date:  1998-08       Impact factor: 10.048

8.  Computation of electromagnetic fields for high-frequency magnetic resonance imaging applications.

Authors:  J M Jin; J Chen; W C Chew; H Gan; R L Magin; P J Dimbylow
Journal:  Phys Med Biol       Date:  1996-12       Impact factor: 3.609

9.  Performing functional magnetic resonance imaging in patients with Parkinson's disease treated with deep brain stimulation.

Authors:  Paula R Arantes; Ellison F Cardoso; Maria A Barreiros; Manoel J Teixeira; Márcia R Gonçalves; Egberto R Barbosa; Sukhi Shergill Sukwinder; Claudia C Leite; Edson Amaro
Journal:  Mov Disord       Date:  2006-08       Impact factor: 10.338

10.  Electromagnetic and modeling analyses of an implanted device at 3 and 7 Tesla.

Authors:  Tamer S Ibrahim; Lin Tang; Alayar Kangarlu; Roney Abraham
Journal:  J Magn Reson Imaging       Date:  2007-11       Impact factor: 4.813

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  21 in total

1.  Assessing the Electromagnetic Fields Generated By a Radiofrequency MRI Body Coil at 64 MHz: Defeaturing Versus Accuracy.

Authors:  Elena Lucano; Micaela Liberti; Gonzalo G Mendoza; Tom Lloyd; Maria Ida Iacono; Francesca Apollonio; Steve Wedan; Wolfgang Kainz; Leonardo M Angelone
Journal:  IEEE Trans Biomed Eng       Date:  2015-12-17       Impact factor: 4.538

2.  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 3.  [MR safety assessment of active implanted medical devices. German version].

Authors:  Sarra Aissani; Elmar Laistler; Jacques Felblinger
Journal:  Radiologe       Date:  2019-10       Impact factor: 0.635

Review 4.  MR safety assessment of active implantable medical devices.

Authors:  Sarra Aissani; Elmar Laistler; Jacques Felblinger
Journal:  Radiologe       Date:  2019-12       Impact factor: 0.635

5.  Construction and modeling of a reconfigurable MRI coil for lowering SAR in patients with deep brain stimulation implants.

Authors:  Laleh Golestanirad; Maria Ida Iacono; Boris Keil; Leonardo M Angelone; Giorgio Bonmassar; Michael D Fox; Todd Herrington; Elfar Adalsteinsson; Cristen LaPierre; Azma Mareyam; Lawrence L Wald
Journal:  Neuroimage       Date:  2016-12-21       Impact factor: 6.556

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.  Polymer thick film technology for improved simultaneous dEEG/MRI recording: Safety and MRI data quality.

Authors:  Catherine Poulsen; Daniel G Wakeman; Seyed Reza Atefi; Phan Luu; Amy Konyn; Giorgio Bonmassar
Journal:  Magn Reson Med       Date:  2016-02-15       Impact factor: 4.668

Review 8.  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

9.  A Virtual Patient Simulator Based on Human Connectome and 7 T MRI for Deep Brain Stimulation.

Authors:  Giorgio Bonmassar; Leonardo M Angelone; Nikos Makris
Journal:  Int J Adv Life Sci       Date:  2014

10.  Temperature control at DBS electrodes using a heat sink: experimentally validated FEM model of DBS lead architecture.

Authors:  Maged M Elwassif; Abhishek Datta; Asif Rahman; Marom Bikson
Journal:  J Neural Eng       Date:  2012-07-04       Impact factor: 5.379

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