Experimental determination of human peripheral nerve stimulation thresholds in a 3‐axis planar gradient system
Bibliographic record
Abstract
In MRI, strong, rapidly switched gradient fields are desirable because they can be used to reduce imaging time, obtain images with better resolution, or improve image signal-to-noise ratios. Improvements in gradient strength can be made by either increasing the gradient amplifier strength or by enhancing gradient efficiency. Unfortunately, many MRI pulse sequences, in combination with high-performance amplifiers and existing gradient hardware, can cause peripheral nerve stimulation (PNS). This makes improvements in gradient amplifiers ineffective at increasing safely usable gradient strength. Customized gradient coils are one way to achieve significant improvements in gradient performance. One specific gradient configuration, a planar gradient system, promises improved gradient strength and switching time for cardiac imaging. The PNS thresholds for planar gradients were characterized through human stimulation experiments on all three gradient axes. The specialized gradient was shown to have significantly higher stimulation thresholds than traditional cylindrical designs (y-axis SR(min) = 210 +/- 18 mT/m/ms and DeltaG(min) = 133 +/- 13 mT/m; x-axis SR(min) = 222 +/- 24 mT/m/ms and DeltaG(min) = 147 +/- 17 mT/m; z-axis SR(min) = 252 +/- 26 mT/m/ms and DeltaG(min) = 218 +/- 26 mT/m). This system could be operated at gradient strengths 2 to 3 times higher than cylindrical configurations without causing stimulation.
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".