Evidence map›Paper›PMID 32756027›Full record

ArticlePhysics in medicine and biology2020

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

Bach T Nguyen, Julie Pilitsis, Laleh Golestanirad

Abstract read
In one paragraph

Article in Physics in medicine and biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed.

  1. Article
  2. Article
  3. Low-field MRI's Spark on Implant Safety: A Closer Look at Radiofrequency Heating.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2023
    Article
  4. Article
  5. Article
  6. Safety of MRI in patients with retained cardiac leads.Magnetic resonance in medicine · 2022
    Article
  7. Article
  8. Predicting RF Heating of Conductive Leads During Magnetic Resonance Imaging at 1.5 T: A Machine Learning ApproachAnnual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2021
    Article
  9. Article
  10. Article
  11. Article
  12. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Bach T NguyenDepartment of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States of America.ORCID 0000-0001-6533-3795
Julie PilitsisDepartment of Neurosciences and Experimental Therapeutics, Albany Medical College, Albany, NY, United States of America.
Laleh GolestaniradDepartment of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States of America.ORCID 0000-0003-3869-6114

Funding

Patient-adjustable MRI technology for high-resolution imaging of deep brain stimulationR00EB021320 · NIBIB · NORTHWESTERN UNIVERSITY AT CHICAGO · PI GOLESTANI RAD, LALEH · 2018 to 2020
$733k
Assessing RF heating of deep brain stimulation implants in high-field open-bore MRI systemsR03EB029587 · NIBIB · NORTHWESTERN UNIVERSITY AT CHICAGO · PI GOLESTANI RAD, LALEH, ROSENOW, JOSHUA M · 2020 to 2021
$158k
Safety assessment of magnetic resonance imaging in patients with retained cardiac leadsR03EB025344 · NIBIB · NORTHWESTERN UNIVERSITY AT CHICAGO · PI GOLESTANI RAD, LALEH · 2018 to 2019
$156k
NIBIB NIH HHS R00 EB021320NIBIB NIH HHS R03 EB025344NIBIB NIH HHS R03 EB029587
6 · The paper itself

Abstract

Numerical simulations are increasingly employed in safety assessment of high-field magnetic resonance imaging (MRI) in patients with conductive medical implants such as those with deep brain stimulation (DBS) devices. Performing numerical simulations with realistic patient models and implant geometry is the preferred method as it provides the most accurate results; however, in many cases such an approach is infeasible due to limitation of computational resources. The difficulties in reconstructing realistic patient and device models and obtaining accurate electrical properties of tissue have compelled researchers to adopt compromises, either to exceedingly simplify implant structure and geometry, or the complexity of the body model. This study examines the effect of variations in anatomical details of the human body model and implant geometry on predicted values of specific absorption rate (SAR) values during MRI in a patient with a DBS implant. We used a patient-derived model of a fully implanted DBS implant and performed numerical simulations to calculate the maximum SAR during MRI at 1.5T (64 MHz) and 3T (127 MHz). We then assessed the effect of uncertainties in dielectric properties of tissue, complexity of body model, truncation of body/DBS model, and DBS lead geometry on SAR. Our results showed that 40% variation in the conductivity of individual tissues in a heterogeneous body model caused a peak of 7% variation in maximum SAR value at 64 MHz, and 10.6% variation in SAR at 127 MHz. SAR predictions from a homogeneous body model with a conductivity range of [Formula: see text] could cover the full range of SAR variations predicted by the heterogeneous body model. Truncation of body model below the implanted pulse generator changed the predicted SAR by 16% at 1.5T and 32% at 3T while saving 250% and 148% in computational time and memory allocation, respectively. In contrast, variation in DBS lead geometry significantly changed the SAR by up to 51% at 64 MHz and 67% at 127 MHz. These results suggest that the error introduced by simplifying the implant's geometry could negate the benefit of using a realistic body model, should such model be used at the expense of oversimplifying implant geometry.

Indexed as

Computer SimulationElectric ConductivityMagnetic Resonance ImagingProstheses and ImplantsDeep Brain StimulationHumans

Identifiers

PMID32756027
PMCPMC8713474

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.