Specific absorption rates and signal‐to‐noise ratio limitations for MRI in very‐low magnetic fields
Bibliographic record
Abstract
Abstract Coil loading experiments were performed to characterize specific absorption rates (SARs) for adult human subjects in uniform linearly‐polarized time‐varying magnetic fields B from 30 kHz to 1.25 MHz, corresponding to a range of Larmor frequencies f that is relevant to MRI in very‐low magnetic fields. For oscillating fields directed perpendicular to the sagittal plane of the human body in the standard anatomical position it was found that $ {\rm{SAR}} = 4.3(1) \times 10^{ - 7} (M/L)f^2 B^2 $ , where M and L are the mass and height of the subject and all quantities are expressed in SI base units. The average linear density M/L appearing in this expression was observed to be an excellent anthropomorphic index for characterizing the manner in which SAR depends on the average transverse dimension of the subject normal to the applied field. As anticipated, SAR values over this frequency range were low compared to those observed at higher frequencies, indicating that emerging applications requiring high duty‐cycle and/or intense radio‐frequency MR tipping pulses will not lead to excessive heating of tissues. Data from these experiments also corroborate and quantify predictions that significant improvements in signal‐to‐noise‐ratios can be achieved through appropriate receive‐antenna design. © 2012 Wiley Periodicals, Inc. Concepts Magn Reson Part A 40A: 281–294, 2012.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".