Direct measurement of magnetic field gradient waveforms
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".