Electric Field Probe for Time-Domain Monitoring of Radio Frequency Exposure During Development and Evaluation of MRI-Conditional Medical Devices at 3 T
Bibliographic record
Abstract
This paper presents the design and validation of a tuned time-domain electric field probe for mapping of radio frequency (RF) exposures used during testing of magnetic resonance imaging (MRI) conditional medical devices. The probes were 5 and 10 mm short dipole antenna, developed as a tradeoff between spatial resolution, linearity, and sensitivity. The probes were tuned and matched at a center frequency of 127.6 MHz, which corresponds to the RF frequency for 3T MRI scanners. To improve the accuracy and sensitivity, an RF low noise amplifier with high gain and very low noise figure was developed, followed by distributed λ/4 baluns along a triaxial cable to reduce the electric field pickup in the MRI environment. The probe was fabricated on a double-sided printed circuit board, FR4 thickness of 1.57 mm and a copper thickness of 35 μm. Theoretical analysis was performed to calculate the exposed electric field from the real-time receive signals. To verify the probe performance finitedifference time-domain method simulations were compared to the actual measured electric fields. Developed probe was tested in a commercially available 3T RF exposure system to determine the probe dynamic range and linearity.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".