Acoustic noise analysis and prediction in a 4‐T MRI scanner
Bibliographic record
Abstract
Abstract Gradient coil induced acoustic noise was measured in a 4 Tesla MRI scanner. To characterize the sound distribution, a series of measurements were taken along the centreline of the gradient coil using echo planar imaging (EPI) sequences as inputs. The acoustic noise frequency spectra were calculated and used to compare the acoustic response relative to the spatial distribution along the centreline. Using the standard approach of frequency response function (FRF) measurement with sinusoidal sweep inputs, the sound pressure levels (SPLs) due to EPI input sequences were predicted. The results showed that the predictions using the FRF were very close to the measured SPLs. Although previously known as an accurate acoustic noise characterization tool, frequency response functions have been shown here to be a useful method for predicting acoustic noise levels due to other scanning sequences. © 2004 Wiley Periodicals, Inc. Concepts Magn Reson Part B (Magn Reson Engineering) 21B: 19–25, 2004.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".