64 MHz RF Exposure System for Testing of Implanted Medical Devices in MRI Applications
Bibliographic record
Abstract
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.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".