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Record W2964563554 · doi:10.1148/radiol.2019190546

Functional MRI Safety and Artifacts during Deep Brain Stimulation: Experience in 102 Patients

2019· article· en· W2964563554 on OpenAlexafffund
Alexandre Boutet, Tanweer Rashid, Ileana Hancu, Gavin J.B. Elias, Robert Gramer, Jürgen Germann, Marisa DiMarzio, Bryan Li, Vijayashankar Paramanandam, Sreeram Prasad, Manish Ranjan, Ailish Coblentz, Dave Gwun, Clement T. Chow, Ricardo Maciel, Derrick Soh, Eric Fiveland, Mojgan Hodaie, Suneil K. Kalia, Alfonso Fasano, Walter Kucharczyk, Julie G. Pilitsis, Andrés M. Lozano

Bibliographic record

VenueRadiology · 2019
Typearticle
Languageen
FieldMedicine
TopicNeurological disorders and treatments
Canadian institutionsOntario Brain InstituteToronto Western HospitalUniversity of TorontoUniversity Health Network
FundersGE Global ResearchWeston Brain InstituteCanada Research ChairsUniversity Health Network
KeywordsMedicineArtifact (error)Deep brain stimulationMagnetic resonance imagingNeuromodulationNeuroimagingNuclear medicineSubthalamic nucleusRadiologyStimulationNeurosciencePathologyInternal medicine

Abstract

fetched live from OpenAlex

Background With growing numbers of patients receiving deep brain stimulation (DBS), radiologists are encountering these neuromodulation devices at an increasing rate. Current MRI safety guidelines, however, limit MRI access in these patients. Purpose To describe an MRI (1.5 T and 3 T) experience and safety profile in a large cohort of participants with active DBS systems and characterize the hardware-related artifacts on images from functional MRI. Materials and Methods In this prospective study, study participants receiving active DBS underwent 1.5- or 3-T MRI (T1-weighted imaging and gradient-recalled echo [GRE]–echo-planar imaging [EPI]) between June 2017 and October 2018. Short- and long-term adverse events were tracked. The authors quantified DBS hardware–related artifacts on images from GRE-EPI (functional MRI) at the cranial coil wire and electrode contacts. Segmented artifacts were then transformed into standard space to define the brain areas affected by signal loss. Two-sample t tests were used to assess the difference in artifact size between 1.5- and 3-T MRI. Results A total of 102 participants (mean age ± standard deviation, 60 years ± 11; 65 men) were evaluated. No MRI-related short- and long-term adverse events or acute changes were observed. DBS artifacts were most prominent near the electrode contacts and over the frontoparietal cortical area where the redundancy of the extension wire is placed subcutaneously. The mean electrode contact artifact diameter was 9.3 mm ± 1.6, and 1.9% ± 0.8 of the brain was obscured by the coil artifact. The coil artifacts were larger at 3 T than at 1.5 T, obscuring 2.1% ± 0.7 and 1.4% ± 0.7 of intracranial volume, respectively (P < .001). The superficial frontoparietal cortex and deep structures neighboring the electrode contacts were most commonly obscured. Conclusion With a priori local safety testing, patients receiving deep brain stimulation may safely undergo 1.5- and 3-T MRI. Deep brain stimulation hardware–related artifacts only affect a small proportion of the brain. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Martin in this issue.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.535

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.015
GPT teacher head0.254
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations77
Published2019
Admission routes2
Has abstractyes

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