Relative Magnetic Force Measures and Their Potential Role in MRI Safety Practice
Bibliographic record
Abstract
BACKGROUND: Magnetic field markings are occasionally used at MRI sites to provide visual feedback of magnetic field strength at locations within the MRI scan room for safety purposes. In addition to magnetic field line markings, relative magnetic force, or ratio of magnetic to gravitational forces on an object, may be considered a useful complementary metric to quantify the risk associated with bringing objects containing ferromagnetic material into the magnetic field. PURPOSE: To develop and validate methods for deriving useful relative magnetic-force measures including a simple force index for application to MRI safety. STUDY TYPE: Phantom. PHANTOM: A special-purpose rig was built to experimentally measure relative magnetic forces on small ferromagnetic objects. FIELD STRENGTH: Ranging from 1.5T to 7T. ASSESSMENT: Quantitative comparisons were made between theoretical and measured relative magnetic forces on six objects containing ferromagnetic material: a piece of iron, a paper clip, a Kelly clamp, nail clippers, a cell phone, and a small permanent magnet. STATISTICAL TESTS: An analysis based on the Bland-Altman method was employed. RESULTS: After correction of the 1.5T data to account for assumed positioning errors of the test rig, limits of agreement between measured and estimated relative forces in the four MRI systems were ±0.16, where a relative force of 1.0 indicates that the magnetic force is equal to gravitation force. There was no significant bias in the data (P < = 0.05). DATA CONCLUSION: Accurate measures of relative magnetic forces on ferromagnetic objects can be derived for MRI safety purposes. LEVEL OF EVIDENCE: 1 Technical Efficacy Stage: 1 J. Magn. Reson. Imaging 2020;51:1260-1271.
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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 | 0.042 | 0.178 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".