Direct measurement of magnetic field gradient waveforms
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract As eddy currents increase with gradient amplitude and faster slew rate, they have become a greater problem with the advent of higher‐performance gradients in modern MRI scanners. Success in eddy current reduction techniques such as active gradient shielding and waveform pre‐emphasis, however, require that the residual eddy currents must be measured with high accuracy for image improvement. Traditional MR based gradient calibration techniques, whether based on an entire FID or a gradient echo, measure the integral of gradient waveforms. We have however previously proposed a different category of methods, which employ pure phase encode FIDs and have proven advantageous in directly measuring the gradient waveforms. In this article, we review the basis of these pure phase encode methods. In keeping with the instructional nature of CMRA, we undertake this review by describing specific experiments and the line of the thought behind the experiments. The pure phase encode approach is sensitive to low amplitude gradients (0.001–1 G/cm), and also permits measurement of high amplitude gradients (10–300 G/cm). The inverse Fourier transform permits ready understanding of these pure phase encode methods. The accuracy of pure phase encode gradient measurement is significantly improved by a multiple FID point acquisition, which permits high temporal resolution of the gradient waveform. The accuracy of gradient measurements is also analyzed and improved through elimination of potential artifacts. As one example of the capability of these methods, waveform measurements were undertaken to reduce the repetition time TR for centric scan SPRITE experiments. © 2010 Wiley Periodicals, Inc. Concepts Magn Reson Part A 36A: 349–360, 2010.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.001 | 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 it