Development of a Transfer-Function Measurement Procedure for the Evaluation's of MRI-Conditional Medical Devices at 3T
Bibliographic record
Abstract
Radio frequency (RF) heating of leads on medically implanted devices is a critical patient safety matter in Magnetic Resonance Imaging (MRI). Safety is generally assessed via large-scale computer simulations, which relies on the use of a “transfer function” (TF) approach in order to make the simulations sufficiently efficient to allow very large numbers of lead trajectories to be considered. In this work, a method to measure the transfer function of a simple stainless-steel wire with insulator was developed, which serves as proof-of-principle for use of the method in more realistic devices. A Finite-difference time-domain (FDTD) method was employed for comparison and to determine the induced electric field near a test wire which was then compared to the measured values. The TF method was applied to 127.6 MHz RF exposure (corresponding to a 3 T MRI system) using a custom developed R F probe in order to improve the accuracy and sensitivity of the measurements. Hydroxyethylcellulose (HEC) gel was used to mimic the lossy tissue environment of the human body. Reasonable agreement between the simulations and measurement were obtained and the method is under development for use at other frequencies of interest.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".