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Record W2979685897 · doi:10.1109/ccece.2019.8861577

64 MHz RF Exposure System for Testing of Implanted Medical Devices in MRI Applications

2019· article· en· W2979685897 on OpenAlexaff
Kieffer Davieau, Ali Attaran, Blaine A. Chronik

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectromagnetic Compatibility and Measurements
Canadian institutionsWestern University
Fundersnot available
KeywordsRadio frequencyElectromagnetic coilRadiofrequency coilHead (geology)Head and neckComputer scienceHuman headBiomedical engineeringElectronic engineeringElectrical engineeringEngineeringMedicineAcousticsTelecommunicationsPhysicsSurgery

Abstract

fetched live from OpenAlex

Currently whole-body transmit RF coils are the most common RF environment for testing. These RF exposure systems designed for testing of devices are commercially available and currently used in a clinical setting; however, even though head-only transmit/receive (TX/RCV) coils are available on MRI scanners, there is not a validated head-only RF exexposure system available for either 64 or 128 MHz. Testing of active implantable medical devices (AIMDs) is guided by the requirements described in ISO 10974:2018(E). Determining the effects radiofrequency (RF) fields have on AIMDS in an MRI system are of paramount importance. Implanted medical devices in the head and neck experience a different local electric field when being imaged head-only exexposure system. This is a significant difference in local electric field when exposed to a head-only transmit coil versus being exposed to a whole-body transmit coil. To adequately evaluate the safety of these devices in that environment, head-only RF exposure systems are needed. In this paper we summarize the steps in developing and validating a head-only RF exexposure system that is properly tuned to 64 MHz and matched to 50 Ω for the testing of implanted medical devices. These steps include the methods for simulating and developing a head-only RF exexposure system.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.642
Threshold uncertainty score0.241

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 head0.016
GPT teacher head0.229
Teacher spread0.213 · 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".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

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