Literature DB >> 24096164

Safe magnetic resonance imaging scanning of patients with cardiac rhythm devices: a role for computer modeling.

Bruce L Wilkoff1, Timothy Albert, Mariya Lazebnik, Sung-Min Park, Jonathan Edmonson, Ben Herberg, John Golnitz, Sandy Wixon, Joel Peltier, Hyun Yoon, Sarah Willey, Yair Safriel.   

Abstract

BACKGROUND: Although there are several hazards for patients with implanted pacemakers and defibrillators in the magnetic resonance imaging (MRI) environment, evaluation of lead electrode heating is the most complex because of the many influencing variables: patient size, anatomy, body composition, patient position in the bore, scan sequence (radiofrequency power level), lead routing, and lead design. Although clinical studies are an important step in demonstrating efficacy, demonstrating safety through clinical trials alone is not practical because of this complexity.
OBJECTIVE: The purpose of this study was to develop a comprehensive modeling framework to predict the probability of pacing capture threshold (PCT) change due to lead electrode heating in the MRI environment and thus provide a robust safety evaluation.
METHODS: The lead heating risk was assessed via PCT change because this parameter is the most clinically relevant measure of lead heating. The probability for PCT change was obtained by combining the prediction for power at the electrode-tissue interface obtained via simulations with a prediction for PCT change as a function of radiofrequency power obtained via an in vivo canine study.
RESULTS: The human modeling framework predicted that the probability of a 0.5-V PCT change due to an MRI scan for the Medtronic CapSureFix MRI SureScan model 5086 MRI leads is <1/70,000 for chest scans and <1/10,000,000 for either head scans or lower torso scans.
CONCLUSION: The framework efficiently models millions of combinations, delivering a robust evaluation of the lead electrode heating hazard. This modeling approach provides a comprehensive safety evaluation that is impossible to achieve using phantom testing, animal studies, or clinical trials alone.
© 2013 Heart Rhythm Society Published by Heart Rhythm Society All rights reserved.

Entities:  

Keywords:  5086 MRI lead; AAMI; Association for the Advancement of Medical Instrumentation; Computer modeling; Hazards; ISO; International Organization for Standardization; Lead electrode heating; MR conditional; MRI; Magnetic resonance imaging; PCT; RF; SureScan; magnetic resonance imaging; pacing capture threshold; radiofrequency

Mesh:

Year:  2013        PMID: 24096164     DOI: 10.1016/j.hrthm.2013.10.009

Source DB:  PubMed          Journal:  Heart Rhythm        ISSN: 1547-5271            Impact factor:   6.343


  9 in total

1.  An optically coupled sensor for the measurement of currents induced by MRI gradient fields into endocardial leads.

Authors:  Eugenio Mattei; Federica Censi; Michele Triventi; Antonio Napolitano; Elisabetta Genovese; Vittorio Cannatà; Giovanni Calcagnini
Journal:  MAGMA       Date:  2014-10-11       Impact factor: 2.310

2.  The Use of Computational Modeling and Simulation to Create Virtual Patients: Application to Cardiac Pacing and Defibrillation Systems.

Authors:  Adam Himes
Journal:  J Cardiovasc Transl Res       Date:  2018-01-05       Impact factor: 4.132

3.  Reducing RF-induced Heating near Implanted Leads through High-Dielectric Capacitive Bleeding of Current (CBLOC).

Authors:  Laleh Golestanirad; Leonardo M Angelone; John Kirsch; Sean Downs; Boris Keil; Giorgio Bonmassar; Lawrence L Wald
Journal:  IEEE Trans Microw Theory Tech       Date:  2019-01-01       Impact factor: 3.599

Review 4.  Destruction Of Medium Already Afected By Destructive Disorder: Fibrillating Atria Conceptually Need Therapeutic Help Rather Than Surgical Or Ablative Destruction.

Authors:  Petras Stirbys
Journal:  J Atr Fibrillation       Date:  2014-06-30

5.  Changes in the specific absorption rate (SAR) of radiofrequency energy in patients with retained cardiac leads during MRI at 1.5T and 3T.

Authors:  Laleh Golestanirad; Amir Ali Rahsepar; John E Kirsch; Kenichiro Suwa; Jeremy C Collins; Leonardo M Angelone; Boris Keil; Rod S Passman; Giorgio Bonmassar; Peter Serano; Peter Krenz; Jim DeLap; James C Carr; Lawrence L Wald
Journal:  Magn Reson Med       Date:  2018-06-12       Impact factor: 4.668

6.  Use of a radio frequency shield during 1.5 and 3.0 Tesla magnetic resonance imaging: experimental evaluation.

Authors:  Christopher P Favazza; Deirdre M King; Heidi A Edmonson; Joel P Felmlee; Phillip J Rossman; Nicholas J Hangiandreou; Robert E Watson; Krzysztof R Gorny
Journal:  Med Devices (Auckl)       Date:  2014-10-29

7.  In silico clinical trials for pediatric orphan diseases.

Authors:  A Carlier; A Vasilevich; M Marechal; J de Boer; L Geris
Journal:  Sci Rep       Date:  2018-02-06       Impact factor: 4.379

8.  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

9.  The cumulative effects and clinical safety of repeat magnetic resonance imaging on an MRI-conditional pacemaker system at 1.5 tesla.

Authors:  Thuy D Nguyen; Sarah A Sandberg; Amir K Durrani; Kevin W Mitchell; Matthew D Keith; Marye J Gleva; Pamela K Woodard
Journal:  Heart Rhythm O2       Date:  2020-12-18
  9 in total

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