Calorimetric calibration of head coil SAR estimates displayed on a clinical MR scanner
Bibliographic record
Abstract
Calorimetric measurements were performed to determine the average specific absorption rates (SAR) resulting from MRI head examinations. The data were compared with average head coil SAR estimates displayed by the MR scanner in order to refine the imaging protocols used in imaging patients with implanted deep brain stimulators (DBS). The experiments were performed using transmit-receive (TR) head coil on clinical 1.5 T General Electric MR scanners running 11.0 M4 revision software. The average applied SAR was derived from temperature increases measured inside a head phantom, due to deposition of RF energy during MRI scanning with a spin echo imaging sequence. The measurements were repeated for varied levels of RF transmit gain (TG) and analyzed with a range of entered patient weights. The measurements demonstrate that the ratio of the actual average head SAR to the scanner-displayed value (coil correction factor) decreases for decreasing TG or for increasing patient weight and may vary between 0.3 and 2.1. An additional retrospective patient study, however, shows that not all combinations of TG and patient weight are encountered clinically and, instead, TG generally increases with the patient weight. As a result, a much narrower range of coil correction factors (e.g., typically 0.5-1.0) will be encountered in practice. The calorimetric method described in this work could aid the physicians and technologists in refinement of the model-dependent SAR estimates displayed by the MR scanner, and in selection of imaging parameters for MR head examinations within allowable SAR safety levels.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".